Automatic Detection of Blood
Vessels in Medical Images
Nikolaos Tzoannou
BSc Computer Science
2012/2013
The candidate confirms that the work submitted is their own and the appropriate credit has
been given where reference has been made to the work of others.
I understand that failure to attribute material which is obtained from another source may be
considered as plagiarism.
(Signature of student) _______________________________
2013
Nikolaos Tzoannou
Summary
The examination and analysis of human tissue in order to diagnose diseases such as cancer is called
histopathology which derives from the greek words of disease suffering (πάθος pathos) and tissue
(ιστός histos). Histopathologists use virtual slides of human tissue which represent a section of the
specific organ tissue which is to be examined. A way to diagnose diseases such as cancer which affect
the circulatory system is to analyse the blood vessels of a specific tissue section. More specifically, a
quantification analysis of blood vessels can provide a large amount of information to
histopathologists. When a human is suffering from cancer there are noticeable differences in the
structure and the number of blood vessels in the specific suffering organ. The digitisation of human
tissue led to the development of several algorithms for microvessel analysis. This project presents and
discusses the performance of a similar commercial algorithm which is used among others by the
Leeds Institute of Molecular Medicine and proposes an improved pipeline to detect blood vessels in
medical images of human tissue. The proposed algorithm is then subject to further evaluation and
discussion.
i
2013
Nikolaos Tzoannou
Acknowledgements
First of all, I would like to thank my supervisor, Dr Derek Magee for his advice, support and guidance
throughout the project and especially his help during the implementation phase. Without his
contribution, the delivery of this piece of work in time would be impossible. I would also like to thank
Dr Darren Treanor and Alexander Wright of Leeds Institute of Molecular Medicine (LIMM) for
helping me to understand and learn about histopathology, tissue staining and all these relevant
technologies. Furthermore, I would like to thank my assessor, Professor Kristina Vuskovic for her
valuable feedback on the mid-project report and during the progress meeting. Finally, I would like to
thank my mother and my father for their invaluable support throughout my degree studies. I dedicate
this piece of work to them.
ii
2013
Nikolaos Tzoannou
Contents
1
Introduction .................................................................................................................................... 1
1.1
Overview ................................................................................................................................. 1
1.2
Aim ......................................................................................................................................... 2
1.3
Objectives ............................................................................................................................... 2
1.4
Minimum Requirements ......................................................................................................... 3
1.5
Deliverables ............................................................................................................................ 3
1.6
Possible Extensions ................................................................................................................. 3
1.7
Schedule and Project Management ......................................................................................... 4
1.7.1
Initial Schedule ............................................................................................................... 4
1.7.2
Revised Schedule ............................................................................................................ 6
1.8
2
Relevance to Degree Programme ............................................................................................ 7
Background Research .................................................................................................................... 8
2.1
Image data ............................................................................................................................... 8
2.1.1
Tissue slides .................................................................................................................... 8
2.1.2
Staining ........................................................................................................................... 9
2.1.2.1
Haematoxylin and eosin (HEM&E) staining .............................................................. 9
2.1.2.2
Haematoxylin and Diaminobenzidine (HEM&DAB) staining ................................. 10
2.1.2.3
Multiple staining methods ......................................................................................... 10
2.1.3
Digitisation .................................................................................................................... 11
2.2
Cancer and the importance of microvessel analysis ............................................................. 11
2.3
Aperio software ..................................................................................................................... 11
2.4
Image pre-processing techniques .......................................................................................... 13
2.4.1
Segmentation by thresholding ....................................................................................... 13
2.4.2
Colour Deconvolution ................................................................................................... 13
2.5
Local pre-processing techniques ........................................................................................... 16
2.5.1
Gaussian smoothing filters ............................................................................................ 16
2.5.2
Edge detectors ............................................................................................................... 18
2.5.2.1
Introduction ............................................................................................................... 18
2.5.2.2
The Laplacian............................................................................................................ 18
2.5.2.3
Zero-crossings of the second derivative .................................................................... 19
iii
2013
Nikolaos Tzoannou
2.5.2.4
The Laplacian of Gaussian ........................................................................................ 19
2.5.2.5
Canny edge detection operator .................................................................................. 20
2.5.3
2.6
3
Morphological operations on binary images ................................................................. 21
2.5.3.1
Dilation and Erosion ................................................................................................. 21
2.5.3.2
Opening and Closing................................................................................................. 23
2.5.3.3
Skeletonisation .......................................................................................................... 23
Region Labeling .................................................................................................................... 24
Project Analysis & System Design .............................................................................................. 25
3.1
Schedule ................................................................................................................................ 25
3.2
Design methodology ............................................................................................................. 25
3.3
Implementation methodology ............................................................................................... 26
3.4
Programming Language ........................................................................................................ 27
3.5
Evaluation ............................................................................................................................. 28
3.5.1 Hausdorff distance ............................................................................................................... 28
4
5
6
Implementation ............................................................................................................................ 29
4.1
Introduction ........................................................................................................................... 29
4.2
Data collection ...................................................................................................................... 30
4.3
Load image into memory ...................................................................................................... 31
4.4
Colour deconvolution............................................................................................................ 31
4.5
Gaussian filter ....................................................................................................................... 31
4.6
Segmentation......................................................................................................................... 32
4.7
Skeletonisation ...................................................................................................................... 33
4.8
Edge extension ...................................................................................................................... 34
4.9
Connected components labelling .......................................................................................... 35
4.10
Edge detection ....................................................................................................................... 36
4.11
Pipeline output ...................................................................................................................... 37
Evaluation ..................................................................................................................................... 38
5.1
Ground truth extraction ......................................................................................................... 38
5.2
Similarity matrix ................................................................................................................... 39
5.3
Blood vessel matching .......................................................................................................... 40
5.4
Matching evidence ................................................................................................................ 41
5.5
Precision and Recall .............................................................................................................. 47
5.6
Observations ......................................................................................................................... 49
Conclusions ................................................................................................................................... 50
6.1
Objectives & Requirements .................................................................................................. 50
6.2
Possible extensions and future work ..................................................................................... 51
iv
2013
Nikolaos Tzoannou
6.3
Conclusion ............................................................................................................................ 52
Bibliography ........................................................................................................................................ 53
A
Personal Reflection ..................................................................................................................... 56
B
External material used ................................................................................................................ 58
C
Detection Evidence ...................................................................................................................... 59
v
2013
Nikolaos Tzoannou
Chapter 1
Introduction
1.1
Overview
According to Cancer Research UK, cancer (of any type) was responsible for 28% of deaths in the UK
in 2010[6]. It has been observed that more than 200 types of human cancer exist [7]. Malignant
tumours, medically referred as malignant neoplasms, commonly known as cancer, are diseases which
mainly involve the creation of new cells in a specific organ abnormally. This also involves blood
vessels since the new created cells require blood supply to grow.
Angiogenesis is the process through new blood vessels are created from existing ones [31]. This
process is completely normal for a human being since is required in development and in wound
healing among other functions. Although this process is vital for humans, it is also observed when
malignant tumours start to grow. Due to the significance of tumour angiogenesis, it is now common to
use angiogenesis inhibitors as part of cancer treatment [12]. By this way, blood supply of malignant
tumours is blocked and therefore, expansion is limited.
Histopathology, which is the examination of human tissue that suffers from some sort of disease, has
been applied to the detection of tumour angiogenesis for several years now. A specific section of an
organ tissue can be examined to determine factors such as the density and the quantity of blood
vessels in order to detect tumour angiogenesis which may result in the diagnosis of cancer.
In the past, histopathologists used to analyse these tissue slides manually using microscopes. The
evolution of machine vision techniques along with the digitisation of sections of human tissue using
immunohistochemistry as explained further in the slide preparation section, has made this process
completely automatic by using certain algorithms and pipelines which analyse these medical images
of human tissue.
1
2013
Nikolaos Tzoannou
This project mainly presents a pipeline based on the commercial algorithm that the Leeds Institute of
Molecular Medicine (LIMM) uses for microvessel detection developed by Aperio, a commercial
ePathology organisation. The proposed algorithm aims to have an improved performance in terms of
accuracy over the already existing pipeline especially in situations when Aperio’s algorithm
performance is poor.
1.2
Aim
The aim of this project is to develop a pipeline of image processing and machine vision techniques,
which use digitised slides of human tissue to analyse and detect blood vessels. The proposed
algorithm should be configurable depending on the type of histopathology images that are used. The
aim is to overcome any flaws of the already existing algorithms and also to propose a solid pipeline
which histopathologists can use to diagnose any type of cancer which involves angiogenesis. Figure
1.1 shows a tissue slide image along with the desired detection that this project aims to achieve.
Figure 1.1: Virtual tissue slide before (left) and after the blood vessel detection (right)
1.3
Objectives
The objectives of the project are to:
•
Research and understand various machine vision techniques and methodologies used in
medical imaging and blood vessel analysis
•
Test existing commercial algorithms and analyse their performance
•
Experiment with different methods of microvessel detection and analysis
2
2013
Nikolaos Tzoannou
•
Design and implement an algorithm for blood vessel detection and analysis in medical images
•
Evaluate the algorithm using different methods. The evaluation is both quantitative (accuracy
based on pathologist supplied ground truth) and qualitative meaning that the method meets
requirements extracted by talking to pathologists about their needs.
1.4
Minimum Requirements
The minimum requirements are:
•
A method for detection of blood vessels in a specific region
•
Design of quantification and characterisation of blood vessels
•
Quantitative accuracy evaluation using ground truth provided by expert pathologists
1.5
Deliverables
•
Implementation of the algorithm
•
MATLAB code
•
Evaluation using Ground Truth
•
Evaluation code
1.6
Possible Extensions
•
XML/text output of the algorithm
•
Visualisation of blood vessel detection
•
Better accuracy for borderline cases by using rotating matching filters
•
Quantification analysis of the tissue slide based on parameters such as size and shape of blood
vessels.
3
2013
1.7
Nikolaos Tzoannou
Schedule and Project Management
1.7.1 Initial Schedule
The initial schedule was set during the second week of the project. It was a first attempt to break
down the different stages of the project aligned to the predefined deadlines that have been given from
the very beginning. The initial fragmentation of the project’s work load was based mainly on
estimates about the difficulty and requirements in terms of time for each of the activities. It was
crucial to make the schedule as flexible as possible in case changes would be necessary throughout
the duration of the project. Having a schedule with detailed deadlines and milestones proved very
helpful and vital for a project of this type.
Figure 1.2 shows a graphical representation of the initial schedule during the 16 week period of the
project along with the milestones that were set.
W3 (04/02) W4 (11/02) W5 (18/02) W6 (25/02) W7 (04/03) W8 (11/03) |4Ws Break| W9 (15/04) W10 (22/04) W11 (29/04) W12 (06/05)
Literature research
Background reading
Experimenting with different methods
Research & discussion
on how to
improve the algorithm
Discover
flaws
Implement improved algorithm
Test
Aperio
alg
using
several
images
Testing and
evaluation
Design
Improved
algorithm
Report Write-up & proof-reading
Mid-Project
report write up
Mid-Project report
submission
Set
Ground
Truth
Tweak,
calibrate
and
improve
Figure 1.2: Initial Project Schedule
4
Final Submission
Evaluate
results
2013
Nikolaos Tzoannou
The milestones and deadlines of the project’s lifetime are described briefly below:

Weeks 1 & 2: Define the aim of the project and set the minimum requirements. Organise the
background reading and literature review and plan an initial schedule for the project.
Background reading on cancer, histopathology techniques and staining methods.

Week 3: Examine and test the performance of Aperio algorithm. Understand how it works
and what methodology utilizes. Background reading and literature research on these methods.

Week 4: Research upon any previous work in the field and similar projects. Read and
understand the theory and methodology that has been used.

Week 5: Experiment with different methods of computer vision that could be used in the
proposed algorithm. As the majority of the background information has been collected,
proceed with the mid-project report write-up.

Week 6: Finalise the background research on relevant literature and complete the mid-project
report write-up.

Week 7: Design an initial implementation of the algorithm using vision methods taken from
the relevant literature and previous similar work.

Weeks 7-9 (including 4 weeks of Easter break): Implementation of the algorithm using an
iterative approach. The implemented system is subject to several iterations during the
development until the result had reached a satisfying level. Moreover, evaluation using
ground truth, provided by the histopathologists, takes place during week 9.

Weeks 10-11: Write up the final report which includes the implementation and evaluation of
the algorithm along with the background reading chapter which was mainly written during
weeks 5 and 6.

Week 12: Final check and proof reading of the report. Submission deadline is on the
Wednesday of this week.
5
2013
Nikolaos Tzoannou
1.7.2 Revised Schedule
Due to several issues that presented during the implementation phase and because an iterative
development approach was used as a guideline, the initial schedule had to be revised in order to meet
the new criteria that appeared especially during the design and implementation phases. The specific
reasons which led to the revision of the initial schedule are presented extensively in the Design
chapter. Figure 1.3 shows a graphical representation of the revised schedule during the 16 week
period of the project along with the milestones that were set.
W3 (04/02) W4 (11/02) W5 (18/02) W6 (25/02) W7 (04/03) W8 (11/03) |4Ws Break| W9 (15/04) W10 (22/04) W11 (29/04) W12 (06/05)
Literature research
Background reading
Experimenting with different methods
Research & discussion
on how to
improve the algorithm
Discover
flaws
Implement improved algorithm
Test
Aperio
alg
using
several
images
Algorithm
evaluation
Design
Improved
algorithm
Testing
Redesign the algorithm
Report Write-up & proofreading
Mid-Project
report write up
Mid-Project report
submission
Set
Ground
Truth
Tweak,
calibrate
and
improve
Final Submission
Evaluate
results
Figure 1.3: Revised schedule
The revised milestones and deadlines of the project’s lifetime are described briefly below:

Weeks 1 to 5: Same as the initial schedule.

Week 6: Finalise the background research on relevant literature and complete the mid-project
report write-up. Start of the design phase with choosing appropriate methodologies.
6
2013
Nikolaos Tzoannou

Week 7: Design an initial implementation of the algorithm using vision methods taken from
the relevant literature and previous similar work. First iteration of the implementation phase
including the reconsideration of the design.

Weeks 7-9 (including 4 weeks of Easter break): Implementation of the algorithm using an
iterative approach. The algorithm is redesigned several times until satisfying methods and
techniques have been chosen. Each design iteration is followed by an implementation phase.

Weeks 10-11: Quantitative and qualitative evaluation of the proposed algorithm. Write-up of
the final report which includes the implementation and evaluation chapters along with the
background reading chapter which was mainly written during weeks 5 and 6.

Week 12: Final check and proof reading of the report. Submission deadline is on the
Wednesday of this week.
1.8
Relevance to Degree Programme
Every aspect of this project presents a significant relevance to the modules I studied during the BSc
Computer Science course. The planning, the design and the development of the project were based on
methodology taught in CR21 (Software Systems Engineering). Furthermore, the majority of computer
vision techniques that were used, are part of AI31 (Computer Vision) module syllabus and most of the
evaluation methods are in extension of AI20 (Artificial Intelligence) module.
7
2013
Nikolaos Tzoannou
Chapter 2
Background Research
In this chapter, background information is provided regarding the data used in the project. Moreover,
some general information about the specific problem of this project is also presented here. More
specifically, a thorough prior art research took place in order to identify possible methods and
techniques already used in the past on similar projects, which will help understand and provide all the
important background knowledge required to address the problem.
2.1
Image data
This section presents the dataset that is used in the project. The data consist of high resolution images
which represent a small section of human tissue. The process of acquiring tissue samples, staining and
digitisation is described in detail in the following sections. These techniques are used by the Leeds
Institute of Molecular Medicine (LIMM) in order to perform microvessel analysis and diagnose
diseases.
2.1.1 Tissue slides
The first step of this process is to surgically obtain a tissue sample from a specific human organ that
histopathologists need to examine. This practice is called biopsy. Then, the tissue sample is enclosed
into paraffin wax in order to be cut into very thin slices. The following steps of this process are to take
8
2013
Nikolaos Tzoannou
these thin slices of tissue and wax, remove the wax using hot water and place the remaining slice of
tissue on a glass slide where the process of staining follows.
2.1.2 Staining
Staining is called the general practice that is used in microscopy where specific dyes are applied on
tissue samples in order to highlight certain structures. These dyes consist of special chemical
substances which react in certain ways when they are applied on certain biological matters such as
proteins, nucleic acid etc. In the case of LIMM staining procedure, it is mostly common to use two
specific staining methods which their names derive from the chemical substances that are used.
2.1.2.1 Haematoxylin and eosin (HEM&E) staining
Haematoxylin and eosin (HEM&E) staining method is a very popular staining protocol which is being
used for many years until now and is one of the most reliable methods to diagnose diseases such as
cancer. Two chemical substances are used. The first one is Haematoxylin which is applied usually
first and the second one is Eosin [23]. Haematoxylin which shows a blue-purple colour, stains nucleic
acids, whereas Eosin has a pink colour and stains cytoplasm proteins [1]. Figure 2.1 shows an
example of placenta tissue that has been stained using the H&E method.
Figure 2.1: Placenta tissue stained with H&E method
9
2013
Nikolaos Tzoannou
2.1.2.2 Haematoxylin and Diaminobenzidine (HEM&DAB) staining
Another very useful staining protocol, especially for the detection of blood vessels, is the use of
Haematoxylin and Diaminobenzidine, an organic compound which when applied to tissue it presents
an oxidation reaction to the iron ion which is contained in the heme groups of haemoglobin [25]. This
reaction produces a brown colour. Tissue sample stained using this method show blue-purple stains
which represent nucleic acids and brown colour stains for protein structures such as haemoglobin.
Figure 2.2 shows a placenta tissue sample stained using the above protocol.
Figure 2.2: Placenta tissue stained with H&DAB method
2.1.2.3 Multiple staining methods
It is possible to apply more than one staining protocols on a single tissue sample when
histopathologists need to visualise and detect multiple antigens. One of the main reasons to perform
multi-staining is the detection of more than one antigen in the same cell [18]. To observe successfully
multiple stains on the same tissue sample, the choice of staining techniques is important because of
the possible cross-reaction between the different staining substances. Moreover, colour combinations
are important in order to achieve higher contrast between them when two or more antigens are
observed [18]. In this project, data produced by multi-staining methods are not used and the main
focus is on H&DAB stained tissue samples.
10
2013
Nikolaos Tzoannou
2.1.3 Digitisation
In order for these tissue samples to be analysed, they require firstly to be digitised. This is performed
at LIMM by high resolution scanners provided by Aperio. These scanners produce high resolution
virtual slides with 20x and 40x magnification capabilities [3] and image resolution which can be up to
55,000 x 50,000 pixels. Consequently, the size of such a virtual slide can be up to 1 GB. For this
reason these slides are stored on servers at LIMM where researchers can access them remotely.
LIMM servers also provide several installed Aperio algorithms for microvessel analysis and detection.
Again, this process can be performed remotely.
2.2
Cancer and the importance of microvessel analysis
As mentioned in the introduction of this report, cancer is called a broad group of diseases that mainly
present unregulated cell growth in a certain human organ. One of the main symptoms of cancer is
angiogenesis. Angiogenesis, which under healthy circumstances is completely normal and vital for the
human, is the procedure of the creation of new blood vessels from existing ones. Angiogenesis is also
observed in a tumours since the new created cells require blood supply in order to evolve. For this
reason angiogenesis can be used as an early prognostic indicator of cancer in a specific organ.
Microvessel density is highly associated with angiogenesis. It is a factor that can determine the
existence of a tumour. Although changes in microvessel density, as a result of angiogenesis are a good
indicator for the diagnosis of cancer, it is not by itself a proof for the existence of cancer [13]. With
this to be considered, studies have proven the importance of angiogenesis and more specifically
microvessel density and its association with epidermal growth factor receptor expression and tumour
size, both being indications of cancer [20].
Another important application of microvessel analysis is the assessment of anti-angiogenic treatment
of cancer. Anti-angiogenic treatment involves the administration of angiogenesis inhibitors as
medicines to the patient. It has been shown that microvessel density shows a decrease after antiangiogenic treatment [29].
2.3
Aperio software
11
2013
Nikolaos Tzoannou
As mentioned before the data which is used consists of high resolution TIFF (SVS) images of around
1GB file size each. They are stored on a server at the Leeds Institute of Molecular Medicine (LIMM)
where the Aperio software is also installed, which allows the user to perform microvessel analysis and
other operations on the images on the same machine, by connecting to this server remotely. In
addition, the user can also download small segments of the images for further investigation. Figure
2.3 shows an example of such an image along with the GUI of Aperio Imagescope program which is
used to run microvessel analysis algorithms. Imagescope has several tools such as markers, region
selection and pen tools which are used to annotate a specific region on the image. The algorithm takes
several parameters as input such as filtering/smoothing level, dark staining threshold, light staining
threshold, region joining parameter, vessel competition parameter, minimum vessel area threshold,
maximum vessel area threshold, maximum vessel wall thickness, output histogram, number of bins,
endothelial stain, background stain etc. The output parameters are: number of vessels, total analysis
area, total stain area, average stain intensity, microvessel density, mean vessel area, median vessel
area, standard deviation of vessel area, histogram results etc. Microvessel analysis using Aperio
software consists of 5 stages [2]:
I.
II.
Scan Digital Slide
Colour Deconvolution
III.
Light and Dark Staining Thresholds
IV.
Completing Vessels
V.
Analysis Metrics
Figure 2.3: Aperio Imagescope
12
2013
Nikolaos Tzoannou
Figure 2.3 shows Aperio software performing the microvessel analysis algorithm on an HEM&E
virtual slide using the default input parameters and generating a markup image. The markup image in
this case consists of annotated regions (green colour). Although the algorithm manages to detect a
number of vessels, it fails to detect another significant number of vessels and it requires a lot of tuning
and tweaking to reach an acceptable level of performance.
2.4
Image pre-processing techniques
2.4.1 Segmentation by thresholding
Segmentation by grey-level thresholding is the simplest method of dividing an image into regions
with similar texture and defined borders. A very common application is the background or foreground
pixel classification. To achieve this, every pixel of the image is converted initially into a single greyscale value by extracting the average value of the three colour values (Red Green Blue), in case of
RGB images, and then a threshold value is defined. Then, we perform a search through every pixel of
the image and if the pixel value is higher than the threshold, it is an object pixel [27]. Otherwise it is a
background pixel. This method is very simplistic and rarely produces good results since a single
threshold for a whole image is not sufficient because of the large variation in grey-level and
background of the majority of the images in the real world. A way to overcome this problem has been
described by Nobuyuki Otsu [22]. Otsu’s method basically proposes that every pixel in the image is
classified as either foreground or background and then it computes an optimal threshold which
minimises the intra-class difference of the foreground and background pixels. In order to achieve this,
it searches for thresholds where the standard deviation of the weighted sum of the variances in the
classes reaches a maximum and it takes the average of these two thresholds. This method yields
satisfactory results even in cases where there is a large variation of greyscale changes in a single
image and can be applied in many practical problems.
2.4.2 Colour Deconvolution
Colour deconvolution is a basic technique which is used to determine the contribution of certain
colours in images. In this case, colour deconvolution is used to calculate the effect of different stain
levels on a virtual tissue slide. As mentioned before, hematoxylin, eosin and DAB stainings are used,
13
2013
Nikolaos Tzoannou
which produce stains that are close to three different colours. Hematoxylin is blue, eosin is magenta
(pink) and DAB is brown. The reason behind this procedure is that these different chemical
substances indicate different biological materials. For example, hematoxylin is mainly used for the
staining of the cell nuclei and eosin is used for the staining of the cytoplasm [24]. The majority of the
slides usually have multiple stains in order to examine different structures and morphologies
simultaneously. Briefly, colour deconvolution as described by Ruifrok and Johnston [24], is
performed by applying an ortho-normal transformation of the RGB information matrix of the optical
density (OD) of each channel for each stain.
Ruifrok and Johnston show that the vector of the optical density of the three channels at a pixel is
defined by the equation below
y=CM
(2.1)
C is the 3x1 vector for the amount of the three different stains at that pixel and M is a normalised OD
matrix for the combination of the three chemical substances.
The theory behind colour deconvolution makes a basic assumption about the colour representation in
these images. It is assumed that grey levels of the RGB channels are linear with the transmission
(brightness) T. T is equal to the incident light over the transmitted light. This assumption is reasonable
for this case.
T = I0/I
(2.2)
Lambert-Beer’s law [15] describes that the intensity of light when passing through a specific stained
specimen (Ic) is given by the equation 2.2. I0,C is the intensity of light when entering this specimen, A
is the amount of stain and c is an absorption factor which characterises every stain for each of the
three RGB channels. The subscript c indicates the detection channel.
IC=I0,C exp(-AcC)
(2.3)
Because the grey level values are non-linear with the intensity values [15], the optical density for each
RGB channel is defined as
ODC =-log10(IC/I0,C)=A*cC
(2.4)
The equation 2.3 indicates the linear relationship between the OD of each channel and the
concentration of absorbing material. For this reason, we use OD to separate the contribution of each
stain colour in the image where every stain has a specific OD in each of the RGB channels. These
values define the OD matrix of a specimen for each of the RGB channels. An example of such a
matrix can be seen below.
14
2013
Nikolaos Tzoannou
Table 2.1: Example of OD matrix for the RGB channels for each stain
As mentioned before to perform colour deconvolution, an orthogonal transformation of the above
matrix must take place. Before that, the matrix must be normalised in order to balance correctly the
absorption factor of each stain. This is done by dividing each OD value by the length of the stain
vector. For the example above, the normalised OD value would be equal to
̂
√
Similarly, a normalised OD matrix is constructed. For this example the matrix should be as shown at
table 2.2.
Table 2.2: Example of OD normalised matrix
The colour deconvolution matrix D, which is the inverse of the above matrix, multiplied by the OD
image as defined by the equation 2.1, yields to the orthogonal representation of the stains which form
the image. For this example the colour deconvolution matrix can be seen below.
Table 2.3: Example of colour deconvolution matrix
The next four figures show how colour deconvolution seperates the contribution of the three different
stains on a tissue slide which has been stained by HEM, Eosin and DAB. This analysis was performed
by Aperio microvessel algorithm using different values of the endothelial stain red, green and blue
parameters in order to obtain the three different contributions.
15
2013
Nikolaos Tzoannou
Figure 2.4: Placenta tissue slide
Figure 2.5: Hematoxylin stain (blue)
Figure 2.6: DAB stain (brown)
2.5
Figure 2.7: Eosin stain (pink)
Local pre-processing techniques
2.5.1 Gaussian smoothing filters
A very important and useful local pre-processing method in computer vision is smoothening by using
Gaussian filters [27]. The purpose of smoothing in general is to remove any noise and other small
fluctuations that usually all images have. The drawback of this technique is the loss of important
information since smoothing also blurs any sharp edges that may define structures important about the
contents of the image. Smoothing techniques can be combined with the use of gradient operators, an
edge enhancement technique, in order to indicate certain locations in the image. These gradient
operators are using the local derivatives of the image function. These derivatives are usually bigger at
certain locations of the image where significant changes in the image function take place and thus
they can be indicated by gradient operators. A disadvantage of gradient operators is that they increase
the level of noise in an image since they suppress low frequencies and noise is of high frequency.
Smoothing is obtained by convolving each pixel of the grayscale image locally using a convolution
16
2013
Nikolaos Tzoannou
mask (kernel) which is a Gaussian function. The equation of a Gaussian function in one dimension is
given by equation 2.5 and in two dimensions is given by equation 2.6 [26][21] where σ is the standard
deviation of the distribution.
(2.5)
(2.6)
An application of this method was made in 1989 by Chaudhuri et al to detect blood vessels in retinal
images [9]. Briefly, they used a matched filter based on two important properties of their dataset.
Firstly, the blood vessels in retinal images have small curvatures and they can be approximated by
linear segments. Secondly, these blood vessels appear to be darker than the background of the image
and their grey level profile on the direction perpendicular to them, can be approximated by a Gaussian
curve which is described by the equation below. A is the grey level intensity of the background, k is a
measure of reflectance of the blood vessel and d is the distance between the point (x,y) and the centre
of the blood vessel.
(
)
(
)
(2.7)
For this reason, a Gaussian kernel was applied to the image in 12 different orientations with 15 o
angular difference between them. By this way, every possible direction of the blood vessels could be
detected. The main advantage of this method is that it smoothens the image while preserving the
continuous structure of the blood vessels and performs relatively well even when the contrast between
blood vessels and background is low. The results of this implementation can be seen in figure 2.8.
Figure 2.8: Typical retinal image (left) and the result of the matched filter application (right)
17
2013
Nikolaos Tzoannou
2.5.2 Edge detectors
2.5.2.1 Introduction
Edge detection operators are a broad set of local pre-processing methods which are used to identify
significant changes in the intensity (brightness) function in an image. Usually, these significant
changes indicate edge pixels. Most of the times, edges of structures and objects in an image are
independent from any changes of viewpoint and illumination. They provide us with solid structure
information, important for the understanding of the content in an image. Although, sometimes due to
the reduction of information caused by edge detectors, significant information might get lost which
results to the misunderstanding of the content.
Mathematically, an edge is vector variable attached to a single pixel which its magnitude indicates the
magnitude of the gradient and direction shows the direction of maximum growth of the function.
Typically, gradient direction is perpendicular to edge direction where edges are usually indicate
region boundaries. Equations 2.8 and 2.9 show how the functions of gradient magnitude and gradient
direction ψ are calculated. The notation arg(x,y) means the angle from axis x to the point (x,y)
calculated in radians [27].
|
(
)|
√( )
(
( ) ,
)
(2.8)
(2.9)
2.5.2.2 The Laplacian
Another linear differential operator that can be used when we are not interested in the direction of the
edge is called the Laplacian. It takes into account only the edge magnitude and it is defined as
(
)
(
)
(
)
.
(2.10)
These edge detectors are tuned depending on the type of edge profile that will be applied on. Figure
2.9 shows some typical edge profiles where these operators can be used [27].
18
2013
Nikolaos Tzoannou
Figure 2.9: Typical edge profiles
2.5.2.3 Zero-crossings of the second derivative
An important category of edge detection operators is those which are based on the zero-crossings of
the second derivative. The basic concept of these operators is that it is easier to find these points
where the first derivative of the image function has a maximum value and consequently the second
derivative should reach zero at that position. The typical procedure which is followed is that, firstly a
smoothing filter is applied in order to remove noise and secondly, the second derivatives are
computed.
2.5.2.4 The Laplacian of Gaussian
In order to obtain smooth filtering while having the response of the filter to be from local points in the
image we use a Gaussian filter. Because of the nature of the Gaussian distribution and since σ
(standard deviation) is the only parameter of such a filter, it is proportional to the size of the
neighbourhood which it operates. Furthermore, pixels located up to 3σ from the centre have influence
on the filtering result.
The combination of applying a Gaussian filter to smooth an image along with the Laplacian operator
to detect the edges is called the Laplacian of Gaussian. The result is to obtain using convolution, a
second derivative of a smoothed 2D function f(x,y) [27]. Equation 2.11 shows the mathematical
expression of such an operator.
(
)
19
(
).
(2.11)
2013
Nikolaos Tzoannou
2.5.2.5 Canny edge detection operator
The Canny edge detector is a special edge detection operator which follows a multi-stage algorithm in
order to obtain the optimal edge detection [8]. Optimality in this aspect is defined by three criteria.
The first one is the detection performance in a sense that every important edge should be detected.
Secondly, the localization criterion suggests that the distance between the edge as detected by the
algorithm and the actual edge should be minimal. The third and last criterion is that the response of a
specific edge should be also minimal. A detected edge should be marked only once and any additional
responses for the same edge should be rejected.
The several stages of the Canny edge detector are the following. First of all, the image data should go
through a noise reduction operation. The convolution with a Gaussian filter of a specific σ value is
such an operation. The reason for this operation is because Canny detector is very sensitive to noise.
The next stage is to identify the intensity gradient of the image. This can be done by any edge detector
which calculates the first derivative values in both the horizontal and vertical direction. This
information is necessary since Canny edge detector uses four filters to detect edges in the filtered
image in four different directions (0, 45, 90 and 135 degrees) that edge directions are rounded to.
The next stage involves a search through all four possible directions and a comparison between the
gradient magnitude and the magnitude of the pixels in that direction. For example, when the rounded
gradient angle is 90 degrees, if a point has a gradient magnitude greater than the magnitudes at the
pixels in the east and west directions, is located on the edge. This stage is called the non-maximum
suppression and results to a set of edge points.
The following stage is called thresholding with hysteresis. False responses caused by noise in the
image can produce false edge contours or even break up an edge contour into small segments. This
effect can be surpassed by setting a low and a high threshold, so the operator always response
between them. More specifically, individual responses higher than the threshold always define edge
pixels and responses above the low threshold are likely to be edge pixels too, if they are connected to
pixels with high responses. Otherwise, individual weak responses are usually noise. By the end of this
last process we obtain a binary image where every pixel is either an edge pixel or a non-edge pixel.
20
2013
Nikolaos Tzoannou
2.5.3 Morphological operations on binary images
There are some useful operations that can be performed on binary images which affect the form or
shape of an object in an image. These operations help us to obtain specific information such as
boundaries or skeletons of an object or a structure.
2.5.3.1 Dilation and Erosion
The main morphological operations on binary images in computer vision are dilation and erosion [30].
Generally, dilation expands an object to a certain degree by filling holes and connecting neighbouring
objects together. Erosion is the opposite operation where the boundaries of an object shrink to a
certain point. Both operations are configurable in terms of the size of the neighbourhood that will be
applied on. For both operations, firstly the structural element must be defined. Typically this has
much smaller size than the original image. Figures 2.10 and 2.11 show an example of a binary image
and a typical structural element.
Figure 2.10: Example of a binary image
Figure 2.11: Typical 3x3 structural element
The mathematical morphology of these operations is based on the set theory where the union of two
sets is the total of elements belonging to either one of the two sets and the intersection is the elements
that belong to both sets at the same time. This can be easily expanded to represent pixels in a binary
image. This can be seen in figures 2.12 and 2.13 [14].
21
2013
Nikolaos Tzoannou
Figure
2.12: Structural elements representing sets A (left) and B (right)
Figure 2.13: Union representation (left) and intersection representation (right) of sets A and B
To perform dilation, a structural element is applied on the image and if the pixel in the centre of the
structural element matches a pixel of the image with the same value, the logical addition of the two
sets takes place for this specific neighbourhood of pixels. Similarly, when performing erosion the
logical disjunction of the two sets takes place. Examples of dilation and erosion for the binary image
in figure 2.10 combined with the structural element in figure 2.11 can be seen in the next figure.
Figure 2.14: Dilation (left) and erosion (right) of the binary image using the 3x3 structural element
22
2013
Nikolaos Tzoannou
2.5.3.2 Opening and Closing
The morphological opening and closing operations are combined sequences of dilation and erosion.
More specifically, opening is defined as the operation of an erosion followed by a dilation and closing
as the operation of a dilation followed by an erosion. Opening can be used to remove pixels of regions
which cannot contain the structural element. Closing is useful when we want to remove gaps or fill in
holes that might exist inside an object. Although both techniques use the same two methods, erosion
and dilation, they yield completely different results due to the order which they are applied. Examples
of morphological opening and closing can be seen in figures 2.15 and 2.16.
Figure 2.15: Example of morphological opening on a binary image using a 3x3 square structuring
element
Figure 2.16: Example of morphological closing on a binary image using a 3x3 square structuring
element
2.5.3.3 Skeletonisation
Skeletonisation on a binary image is the process of extracting a subset of the original object which
shows the fundamental structure of it. The skeleton consists only of single pixels which are connected
together, expanding towards every direction without having any gaps between them, according to the
23
2013
Nikolaos Tzoannou
shape of the object which is being skeletonised. A common method to perform skeletonisation is to
iterate a thinning algorithm, usually sequences of successive erosions, while preserving the
connectivity of the pixels until no further change can be made. In general an optimal skeleton must
comply with the three following criteria [19]:
a. The skeleton should be connected and consist of single pixels only.
b. Erosion beyond the point of which a feature is represented should be detected and avoided.
c. The skeleton should not be affected by noise or any other small convexities which are not part
of the object’s shape.
Figure 2.17 shows an example of skeletonisation performed on a simple binary image. It is noticeable
that the resulting skeleton is connected and indicates the shape of the original object.
Figure 2.17: Example of skeletonisation on a simple binary image
2.6
Region Labeling
Connected component labelling in binary images is an algorithmic method which is widely used in
computer vision applications in order to detect connected regions and uniquely label them. Generally
the algorithm goes through the image twice. Thus, it is called two-pass algorithm. During the first
pass it identifies equivalences and labels each pixel temporarily. During the second pass it labels
again each pixel with the final label of its equivalence class. The algorithm can use either 4connectivity, which means that connectivity checks are performed only between the north and the
west neighbour of each pixel, or 8-connectivity where pixels north-east and north-west of current
pixel are additionally checked. Because the algorithm creates continuously new labels, it can be
significantly slow. In order to speed up this process, the algorithm uses a disjoint-set data structure
[10]. This data structure provides an interface which makes it easier to keep track of equivalence
relationships.
24
2013
Nikolaos Tzoannou
Chapter 3
Project Analysis & System Design
3.1
Schedule
In order for a project to be successful and deliver in time, it is crucial to have a solid plan which will
act as a guide throughout the project’s lifetime. As mentioned in the introduction, the workload of the
project was divided according to estimations of the amount of time needed for each of the different
stages. Initially, the design stage of the project was scheduled for the 7th week. Due to certain
characteristics of this project, the design phase continued in the weeks after. More specifically, most
of the design of the algorithm took place along with the early stages of the implementation. There
were two main reasons for this practice. Firstly, the initial design phase was based on estimations.
Secondly, during the actual implementation, methods and techniques that were originally decided to
be included in the pipeline, were proved inefficient which led to the need of redesigning important
stages of the implemented system. This was the major change which occurred upon the initial plan for
the whole project and affected significantly the implementation phase. Consequently, the
implementation stage lasted longer, leaving less time for the evaluation and the report write-up as it
was shown in the revised schedule.
3.2
Design methodology
In order to design the proposed pipeline, several experiments and tests took place. Initially, Aperio
algorithm was tested in order to determine its performance. Aperio provides the users with the
pipeline that uses, although it is very abstract since it is written for histopathologists and physicians.
Most of the technical details and methodologies are hidden from the users providing only a set of
adjustable parameters. After extensive experimenting on several tissue slides with a variety of stains,
25
2013
Nikolaos Tzoannou
it was found that Aperio software performed optimally only after excessive tweaking of its
parameters. Furthermore, different parameterisation was needed for each case depending on the stain
used, type of tissue and size of blood cells. The post-experiment analysis showed that Aperio
algorithm failed to classify significantly large and connected blood vessels. An example of this poor
performance was presented in Figure 2.3 of the background research chapter.
The first design step was to experiment with the methods and techniques presented in Chapter 2 on
the specific dataset of this project. Colour deconvolution was the first method that was tested
extensively on a set of 10 different placenta tissue slides stained with HEM&DAB. Same as the
Aperio algorithm, it was decided to include it as the initial pre-processing method of the implemented
pipeline since it performed significantly well in extracting the desired stain from the image. Similarly,
inspired by the algorithm regarding the detection of blood vessels in retinal images, which was
presented before, a filtering method was found to be necessary. The reason for including a filter is that
noise and unnecessary information needed to be removed while at the same the implemented system
was aiming to detect certain structures in the images (i.e. closed elliptical shapes).
The design of the Gaussian filter itself was made during the implementation process. Due to certain
difficulties in obtaining optimal results, the filter had to be redesigned many times during the
implementation of the algorithm. This was also the case for the morphological methods since they
were depending heavily on the results of the previous stages of the pipeline.
3.3
Implementation methodology
There were two important factors that determine the development methodology that was followed.
First of all, the lack of any prior experience in large scale projects and more specifically in designing
and implementing a system of such kind, made it hard to predict the necessary requirements and
features of the implemented algorithm. Secondly, due to the complexity of the images in terms of the
large variance in structure of blood vessels between different tissue slides along with the significant
amounts of noise in the images, the product needed to be redesigned, implemented and tested several
times to reach an acceptable level of performance.
For the above reasons, iterative development was selected as the appropriate model of development.
Iterative development is an agile software engineering methodology very popular in the recent years
due to the flexibility it provides, especially in cases where limited factors can be predicted in advance.
The main idea consists of repeated cycles (iterations) of the design, implementation, testing and
possibly evaluation stages of the development of a system, in order to achieve the optimal final result
26
2013
Nikolaos Tzoannou
[17]. Each iteration provides the developer with essential new information, obtained by using and
testing the present version of the system, which can be used in the next improved version and usually
each cycle adds new functional capabilities. Although the iterative model usually includes the
reconsideration of the system requirements, in this case this was not necessary.
The most important advantage of iterative approach is that it delivers an early version of the product
sooner than other methodologies, which the developer can work on it and improve. In extend of this,
it allows early improvements from the first iterations. Furthermore, improvements are accurate since
they are based on the experience gained from the most recent testing phase.
3.4
Programming Language
Choosing the suitable programming language for implementing the system was very crucial since it
can affect the result of the project in a large scale. Throughout my studies during my Computer
Science degree, several languages were used (Python, Java, C, Matlab etc), each of them being
significantly better in certain projects and applications than the others mainly because of the different
characteristics each one has, such as being object oriented or functional, high level or low level.
Computer vision applications are usually implemented in C, C++ or Matlab with C being the fastest
and most efficient since it is a low level language. However for this project MATLAB was chosen for
the following reasons:

My exposure to Matlab was significantly larger than any other language during my degree.
More specifically, labs and assignments for the Computer Vision module used Matlab. Thus,
my confidence in implementing the system in Matlab was much higher.

Since Matlab is not as low level as C it requires less complex coding which means less time
spending in coding and debugging, both procedures being very time-consuming when
implementing a system in a low level language. The limited overall time for the project made
it almost necessary to use Matlab.

Matlab provides the user with many built-in functions for image processing such as the Image
Processing Toolbox which makes it much easier to implement certain computer vision
techniques.

Matlab uses matrices (arrays) as the main data structure which makes it even more suitable
for image processing, since images are in a sense, large matrices.

Plotting abilities of Matlab makes it easier to visualise the results of the evaluation stage.
27
2013
3.5
Nikolaos Tzoannou
Evaluation
The quantitative evaluation of the algorithm involves the comparison of the results obtained from the
implemented proposed algorithm with the Ground Truth as provided by expert pathologists. More
specifically, the algorithm is tested using 10 different tissue slides stained with HEM&DAB. The final
output of the implemented pipeline produces a text file which contains all the regions of the image
which have been identified as blood vessels. Ground truth is obtained with the help of Aperio
Imagescope. The same tissue slides that have been used to test the algorithm, are hand-annotated by
pathologists, marking the blood vessels that actually exist and should be detected. Imagescope
produces an XML file containing these annotated regions. Through an XML parser, another text file is
produced containing this information about the regions in the same form of the results text file. These
two text files are then read by the evaluation program which computes the Hausdorff distance
between the Ground Truth and the algorithm result for each region (i.e. blood vessel). One of the final
steps of the evaluation involves the completion of the matching between the ground truth regions and
the detected regions and also the identification of any false detections of the implemented system.
This is done by an implementation of the Hungarian algorithm which matches regions of the Ground
Truth and regions of the results which have the lowest Hausdorff distance between them. Furthermore
a threshold is defined where any matching above it, is classified as false matching. The final phase of
the evaluation involves the visualisation and the statistical analysis of the results.
3.5.1 Hausdorff distance
The Hausdorff distance is a mathematical metric which computes the distance between two subsets. It
is the longest distance between any two points of these two subsets [28]. It is used in computer vision
to determine the similarity of a structure to a specific template in binary images. Hence, if the
Hausdorff distance between a set of points of a structure in a binary image and a set of points of a
template is zero, the two sets are identical and they match. Similarly if it is very large, the two sets
represent different shapes or structures.
28
2013
Nikolaos Tzoannou
Chapter 4
Implementation
4.1
Introduction
The initial design of the algorithm suggested that the pipeline would have as its initial stage the colour
deconvolution of the tiusse slide. Thereafter, the desired stain would go through a filter which will
enhance the structures we wish to detect. The next stages of the pipeline, involve the detection of the
blood vessels using morphological operations on binary images. Finally the pipeline would output a
text file containing information about the blood vessels that have been detected.
In this chapter the implementation phase of the project is presented in detail. A flowchart presenting
the algorithm’s pipeline can be seen in figure 4.1. As mentioned before, large part of the design took
place along with the implementation, specially the design of the Gaussian filter and the morphological
operations that were necessary. This iterative approach proved to be very efficient since the initial
requirements were met, although it was time consuming which had as a result the implementation
phase to last longer than the initially planned time.
29
2013
Nikolaos Tzoannou
Figure 4.1: Pipeline flowchart of the proposed algorithm
4.2
Data collection
LIMM stores hundreds of stained tissue slides on its servers providing the ability for researchers to
examine them and run several experiments on them. For the implementation of the algorithm we used
a virtual tissue of a pathological placenta stained with HEM&DAB, since the DAB stain is used to
highlight the existence of blood vessels. Again for the implementation phase a small segment of the
tissue slide was used instead. The resolution of the image was 686x991 pixels and because it was
magnified 20 times, the actual size of the tissue segment was approximately 650 μm to 350 μm (1 μm
= 10-4 cm).
30
2013
4.3
Nikolaos Tzoannou
Load image into memory
The first step of the pipeline is to load the tissue slide image into the memory. The image is stored in
an array (matrix) data structure. The matrix has the same size as the image in pixels. Each element of
the matrix represents a pixel in the image and contains three values, one for each colour (RGB). The
data is now ready to be pushed to the next stage of the pipeline.
4.4
Colour deconvolution
This stage of the pipeline involves the colour deconvolution of the original image in order to acquire
the contribution of each stain. For the implementation, a colour deconvolution function written in C
by Tom Macura which was compiled as a Matlab function, was used. This particular function
provides the user with the ability to select, based on a specific argument, the type of stain that he/she
is interested in. Figure 4.2 shows the original image of the tissue slide and the DAB stain grayscale
image obtained from colour deconvolution. The stain image is stored in memory and it passes to the
next step of the pipeline.
Figure 4.2: Original tissue slide (left) and the DAB stain contribution (right)
4.5
Gaussian filter
The next step of the algorithm involves the convolution of the stain image with a specific Gaussian
filter. The equation 2.7 in chapter 2 describes the Gaussian function that is used. A standard deviation
31
2013
Nikolaos Tzoannou
of σ = 0.1 and a distance of d=10 pixels were chosen since, after experimenting, it was found that with
these values, even very small blood vessels (with a diameter ≈ 10 pixels) are enhanced and a
significant amount of noise is removed without losing any important information. The results of the
Gaussian filter convolution are shown in figure 4.3.
Figure 4.3: The result of the Gaussian filter convolution (right) on the DAB stain image (left)
4.6
Segmentation
The next stage of the pipeline is to pull the convolved image and create a binary image from it. The
necessity of a binary image lies in the fact that it is much easier to perform morphological operations
on them and detect structures and contours. The initial design of segmentation suggested choosing an
appropriate threshold based on Otsu’s method as it is described in Chapter 2. Again, after several tests
on different images it was found that due to the particularity of these images, Otsu’s method proposed
a threshold which performed rather poorly in separating the foreground from the background. Hence,
the segmentation step was redesign and a threshold based on experiments with different images was
chosen. After extracting the binary image, it is stored into memory and pushed to the next step of the
algorithm. The result of binarisation can be seen in figure 4.4.
32
2013
Nikolaos Tzoannou
Figure 4.4: Segmentation of the grayscale image by thresholding
4.7
Skeletonisation
After extracting the binary image, the next stage is the morphological operation of skeletonisation in
order to extract the basic shape of the contours of the blood vessels. The result was to extract the
majority of skeletons in the image without breaking apart any objects. However some objects lost
their connectivity which led to the need of implementing a way of connecting any hanging edges and
closing the open structures. The next figure shows the result of the morphological operation of
skeletonisation on the binary image.
Figure 4.5: The result of performing skeletonisation on the binary image
33
2013
4.8
Nikolaos Tzoannou
Edge extension
As mentioned before, due to the fact that some contours lost their connectivity, a method that will
close any disconnected edges needed to be implemented. The basic idea behind the method that was
implemented is to extend the end points of each edge, towards the direction they tend to have until it
reaches another pixel of the foreground. More specifically, if a certain 3x3 pixel neighbourhood has a
certain form that shows continuity to a certain direction, then this neighbourhood is reverted and by
using a logical AND operation is added to the original neighbourhood. By this way, the extension of
the edge towards the trending direction is accomplished. An example of this algorithm is shown
below.
reversion
logical AND`
Figure 4.6: Example of edge extension algorithm
Figure 4.7 shows the results of the above algorithm on the skeletonised image obtained at the earlier
stage of the pipeline. It can be seen that the majority of the disconnected contours are now connected.
Furthermore the algorithm produced some unnecessary extra edges which will be removed at a latter
stage.
Figure 4.7: The skeletonised image before the edge extension (left) and after the extension (right)
34
2013
4.9
Nikolaos Tzoannou
Connected components labelling
The next step of the algorithm is to label the connected components in order to find and extract every
connected region in the image. These regions should now represent the blood vessels as they have
been detected. In order to implement this operation, the built-in connected components labelling
function of the Image Processing Toolbox of Matlab was used. To avoid neighbouring objects to be
considered as one, 4-connectivy was used instead of 8. An example of this operation is shown in
figure 4.8. It is noticeable that if 8-connectivity was used, objects 2 and 3 would be considered as one
single object [16].
Figure 4.8: Example of bwlabel Matlab function on a binary image containing 3 connected
components
The connected components regions as identified are shown in figure 4.9. For visualisation purposes,
each region is labelled with a different colour.
35
2013
Nikolaos Tzoannou
Figure 4.9: Labelled regions as they have been identified by Matlab’s connected components function
4.10 Edge detection
At this stage of the algorithm, according to the initial design a Canny edge detector would be applied
in order to detect the edges of every region and identify the blood vessels. During the first iterations of
the implementation and after excessive testing, it was decided that Canny edge detector was not
suitable. Its performance was poor since it failed to identify every edge and on top of that, a
significant amount of the noise which was created during the extension phase was identified as edges.
Instead of a Canny detection operator, an implementation of Moore-Neighbour tracing algorithm to
trace the exterior boundaries of the detected regions was used. The idea behind the Moore-Neighbour
tracing algorithm is the following [11]:

A neighbourhood of 8 pixels around a starting pixel is defined. These are the 8 directions
including the diagonals.

Cycle either clockwise or anticlockwise (it makes no difference as long as it is consistent
throughout the algorithm) through the 8 pixel neighbourhood beginning from the starting
pixel.

The first foreground pixel that is found, mark it as a boundary pixel.

Cycle again through the neighbourhood of the previously detected boundary pixel.
36
2013

Nikolaos Tzoannou
Repeat the same process until every pixel in the image has been searched in a similar manner
4.11 Pipeline output
The last stage of the implemented pipeline is to visualise the results for the qualitative evaluation of
the procedure and the extraction of the detected region information to a text file for the quantitative
evaluation. In order to extract the data needed for the quantitative evaluation, a loop through each
region was implemented extracting the coordinates of every point of the detected boundaries and
storing them to a text file. The text file contains three columns. The first two columns are the x and y
coordinates of a pixel and the third column contains a number representing the region that this pixel
belongs. Figure 4.10 shows the visualisation of the final output of the pipeline plotted on top of the
original tissue image.
Figure 4.10: Final output of the implemented algorithm for blood vessel detection. The marked
regions in green colour annotate the detected blood vessels.
37
2013
Nikolaos Tzoannou
Chapter 5
Evaluation
For the quantitative evaluation of the implemented system a piece of code was written which receives
as input the two text files, one for the Ground Truth and one for the results of the detection, containing
the region information. From these two files, the evaluation program computes the similarity between
the two sets and matches the detected blood vessels to the ones from the Ground Truth. This
evaluation program was also implemented in Matlab since it is easier to handle and perfrom
operations in large matrices. In this chapter, a detailed description of the evaluation process is
presented along with some statistical analysis of the evaluation results.
5.1
Ground truth extraction
As we described before, hand-annotated images of the tissue slides were produced prior the detection,
which contain the Ground truth for this specific segment of tissue. A total of 10 different segments of
tissue slides were used for the evaluation of the proposed algorithm. Using Aperio Imagescope an
XML file for each tissue slide was extracted containing the coordinates of the pixels of every blood
vessel according to the ground truth. Then, using an XML parser written in C by Frank Vanden
Berghen [4], 10 text files have been created from the XML files.
38
2013
5.2
Nikolaos Tzoannou
Similarity matrix
With the use of the coordinate information for both the ground truth and the detected blood vessels,
the evaluation program produces a similarity matrix based on the Hausdorff distance between every
blood vessel from the ground truth and every vessel of the detected regions set. In detail the process is
the following.
Firstly, the two sets are read from the two text files; one for the ground truth and one for the detected
blood vessels. An example of the format of these files is shown below.
Table 5.1: Example of the format used for the region information text files. The first column contains
the X coordinate, the second column the Y coordinate and the third column indicates the region that
this point belongs.
The similarity program outputs a two-dimensional matrix where each i,j element of the matrix
indicates the Hausdorff distance between the ith region of the ground truth set and the j th region of the
detected vessels set. If the Hausdorff distance between two regions of the two sets is zero, it means
that the two regions are exactly the same and this particular blood vessel has been 100% detected
properly. Similarly, if the Hausdorff distance is very large, the two regions are irrelevant. A
visualaisation of a similarity matrix is show in figure 5.1. The vertical axis represents the detected
blood vessels set and the horizontal axis represents the ground truth set. Warm colours indicate a high
Hausdorff distance value which means low similarity between the two regions. Similarly, cold colours
indicate low Hausdorff distance value, meaning high similarity between the two regions.
39
2013
Nikolaos Tzoannou
Figure 5.1: Visualisation of the similarity matrix between the two sets
5.3
Blood vessel matching
The next stage of the quantitative evaluation process is the optimal matching between the two sets of
blood vessels. In order to accomplish this, a Matlab implementation of the Hungarian algorithm is
used written by Markus Buehren [5]. This algorithm matches each blood vessel of the ground truth set
to a vessel of the results set. The implementation of this algorithm returns an assignment column
vector which contains the matching blood vessel of the results set to each vessel of the ground truth
set. An example of this vector can be seen in figure 5.2. In this example, the 9th region (blood vessel)
of the results set is matched to the 1st region (blood vessel) of the ground truth set, the 76th to the 2nd
etc. It is noticeable that in line 18 the value is zero. This means that no blood vessel from the detected
ones matches with the 18th vessel of the ground truth. For optimal results, a threshold is applied on the
similarity matrix in order to remove completely irrelevant regions. This threshold was found
experimentally and was usually between 20 to 40 pixels depending on the image. Consequently, if the
Hausdorff distance was larger than this threshold, the value for these two regions in the similarity
40
2013
Nikolaos Tzoannou
matrix was set to infinity. By this way, there is no attempt at all to match this particular pair of regions
by the matching algorithm.
Figure 5.2: Example of assignment column vector as produced by the Hungarian algorithm
implementation
5.4
Matching evidence
In order to have a representative analysis of the evaluation, some statistical features need to be
extracted and discussed. First of all, 10 different tissue slide segments were used. For each of these
images, the similarity matrix and the matching allocation were computed. From the number of
repetitions of 0 in the assignment vector, we can determine an initial amount of false positives (i.e.
blood vessels that have been falsely detected, having no matching in the ground truth set). The
remaining blood vessels appear to have a matching in the ground truth set.
41
2013
Nikolaos Tzoannou
In the next pages, a statistical analysis for the 10 image samples that were used for the evaluation is
presented. This includes evidence of the detection and the matching along with a table containing the
numbers of True Positives (detected and matched blood vessels), False Positives (detected but not
matched blood vessels), detected blood vessels in total (TP + FP) and False Negatives (undetected
blood vessels according to the ground truth).
Figure 5.3: Visualisation of matching and detection for tissue sample (a). On the left image are shown
the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance in the
similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in pink
colour are the matched to the ground truth blood vessels (true positives).
Figure 5.4: Visualisation of matching and detection for tissue sample (b). On the left image are shown
the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance in the
similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in pink
colour are the matched to the ground truth blood vessels (true positives).
42
2013
Nikolaos Tzoannou
Figure 5.5: Visualisation of matching and detection for tissue sample (c). On the left image are shown
the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance in the
similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in pink
colour are the matched to the ground truth blood vessels (true positives).
Figure 5.6: Visualisation of matching and detection for tissue sample (d). On the left image are shown
the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance in the
similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in pink
colour are the matched to the ground truth blood vessels (true positives).
43
2013
Nikolaos Tzoannou
Figure 5.7: Visualisation of matching and detection for tissue sample (e). On the left image are shown
the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance in the
similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in pink
colour are the matched to the ground truth blood vessels (true positives).
Figure 5.8: Visualisation of matching and detection for tissue sample (f). On the left image are shown
the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance in the
similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in pink
colour are the matched to the ground truth blood vessels (true positives).
44
2013
Nikolaos Tzoannou
Figure 5.9: Visualisation of matching and detection for tissue sample (g). On the left image are shown
the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance in the
similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in pink
colour are the matched to the ground truth blood vessels (true positives).
Figure 5.10: Visualisation of matching and detection for tissue sample (h). On the left image are
shown the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance
in the similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in
pink colour are the matched to the ground truth blood vessels (true positives).
45
2013
Nikolaos Tzoannou
Figure 5.11: Visualisation of matching and detection for tissue sample (i). On the left image are
shown the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance
in the similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in
pink colour are the matched to the ground truth blood vessels (true positives).
Figure 5.12: Visualisation of matching and detection for tissue sample (j). On the left image are
shown the detected blood vessels, annotated in green colour, after thresholding the Haussdorf distance
in the similarity matrix. In yellow colour is shown the ground truth. On the right image, annotated in
pink colour are the matched to the ground truth blood vessels (true positives).
46
2013
Nikolaos Tzoannou
For each tissue sample the evaluation program outputs its results which are presented in the table 5.2.
From this information a statistical analysis is also presented using the concepts of precision and recall
in order to evaluate the accuracy of the algorithm.
Tissue
Ground Truth
samples
Detected in
Matched blood
Unmatched
Undetected
total (TP+FP)
vessels (TP)
blood
blood vessels
vessels (FP)
(FN)
a
77
144
61
83
16
b
39
100
35
65
4
c
33
83
27
56
6
d
52
102
52
50
0
e
35
75
27
48
8
f
43
132
40
92
3
g
27
53
22
31
5
h
31
66
30
36
1
i
33
53
29
24
4
j
31
71
27
44
4
Table 5.2: Table of True Positives, False Positives, detected blood vessels in total (TP + FP) and False
Negatives for each tissue sample
5.5
Precision and Recall
In statistics and especially in classification problems, precision and recall are widely used to evaluate
the performance of a classification process. In this context precision is defined as
and recall as
. Consequently, when precision is equal to 1, it means that there no false
positives at all. When recall is equal to 1, it means that FN = 0, hence the algorithm has detected
successfully every blood vessel in the ground truth. These two statistical measures are rarely
presented in isolation. Another measure it is used, which is called the f-measure (or f-score). F47
2013
Nikolaos Tzoannou
measure combines precision and recall to compute a harmonic mean in order to measure the accuracy
. The
of the classification. The definition of f-measure is the following.
following table contains the precision and recall values along with the f-score for each tissue sample.
Figure 5.13 shows a very informative Recall-Precision diagram including the f-measure values as
contour lines for each of the tissue samples.
Tissue sample
Precision
Recall
F-score
a
0.42
0.79
0.55
b
0.35
0.89
0.50
c
0.32
0.81
0.46
d
0.51
1.00
0.67
e
0.36
0.77
0.49
f
0.30
0.93
0.45
g
0.41
0.81
0.55
h
0.45
0.96
0.62
i
0.54
0.88
0.67
j
0.38
0.87
0.53
Table 5.3: Precision, Recall and F-score table for each tissue sample
48
2013
Nikolaos Tzoannou
Figure 5.13: Precision – Recall diagram containing the f-score for each tissue sample
5.6
Observations
It is noticeable that the proposed algorithm in almost all cases over-detects blood vessels, producing a
large amount of false positives along with the true detected vessels. A possible reason for this
behaviour could be the design of the Gaussian filter in the early stages of the pipeline. It seems that it
needs further re-adjustments to successfully remove any noise and enhance the desired structures.
Consequently, the edge extension stage of the pipeline, has created false edges from this noise which
resulted in producing new connected components which the algorithm falsely detects as blood vessels.
On the other hand, in almost all cases the algorithm manages to detect the vast majority of the blood
vessels which are included in the ground truth set. For example, in tissue sample (d) the algorithm
reaches a recall value of 100% which means that every blood vessel in the ground truth set has been
successfully detected.
49
2013
Nikolaos Tzoannou
Chapter 6
Conclusions
The overall aim of this project was to present a pipeline of computer vision and image processing
techniques in order to detect blood vessels in medical images of a certain type. The qualitative
evaluation showed that the implemented algorithm performs well since there are very few undetected
blood vessels. The quantitative evaluation showed that the algorithm succeeds in detecting the vast
majority of blood vessels in the tissue slide, although it produces a significant amount of false
positives.
6.1
Objectives & Requirements
The objectives and minimum requirements defined in chapter 1 have been completed. The extensions
described in chapter 1 have also been completed to a certain degree. More specifically, the aim and
objectives have been met as described below:

Research and understand the computer vision methods and techniques used in the field of
histopathology and blood vessel detection has been accomplished as described extensively in
chapter 2.

The testing and the qualitative evaluation of the commercial algorithm which LIMM uses has
also been accomplished as presented in chapter 2.
50
2013
Nikolaos Tzoannou

Experimenting with different methods of microvessel detection and analysis for learning,
understanding and design purposes of the proposed algorithm has taken place. Chapters 2 and
3 describe this process thoroughly.

The design and the implementation of the proposed algorithm have been accomplished. The
implementation meets the minimum requirement which is a method for detecting blood
vessels in medical images. Moreover, it has met the extensions up to a certain degree, since it
visualises the detection and also outputs a text file with quantitative information of the
detection. The design and implementation phases are presented thoroughly in chapters 3 and
4. Evidence of blood vessel detection is presented in Appendix C.

Quantitative accuracy evaluation using ground truth provided by expert pathologists has been
accomplished as described in chapter 5. Qualitative evaluation has also taken place by
providing to pathologists examples of the algorithm’s output.
6.2
Possible extensions and future work

One possible extension for further development of the system would be to produce an XML
file from the output of the detection algorithm. This XML file, assuming that agrees with the
format used by Aperio Imagscope could be used for importing the annotated regions and
visualise them on Imagescope. This feature can be very useful for the pathologists,
considering that they rely heavily on this piece of software to analyse and visualise blood
vessel detection.

Another extension would be the calculation of the area (in μm2) which is covered by blood
vessels in this specific tissue segment. Pathologists could use this information to diagnose
angiogenesis in a certain organ by comparing tissue samples from different time periods.

The algorithm could be extended to detect other structures besides blood vessels, assuming
that there is a suitable staining method to perform colour deconvolution and some ground
truth is provided to design the necessary filters.

A last possible extension could be to provide a supervised machine learning interface where
the system will learn from the results that produces which then are evaluated by the user. By
this way, the detection and classification of blood vessels would be significantly improved in
terms of accuracy.
51
2013
6.3
Nikolaos Tzoannou
Conclusion
This project was about developing and evaluating an alternative pipeline to the algorithm used by
histopathologists at LIMM for blood vessel detection. The implemented algorithm uses a series of
computer vision methods which were described extensively and proved to be suitable for this
application. The evaluation of the algorithm showed that meets the original criteria and it performs as
expected, although there is still room for improvement.
52
2013
Nikolaos Tzoannou
Bibliography
[1] Andrew H. Fischer, Kenneth A. Jacobson, Jack Rose and Rolf Zeller. Hematoxylin and Eosin
Staining of Tissue and Cell Sections. Cold Spring Harbor Protocols, doi:10.1101/pdb.prot 4986,
2008.
[2] Aperio Algorithm User Guide: Microvessel Analysis, the University of Chicago, 2011
http://htrc.uchicago.edu/APERIO/downloads/HTRC_microvesselUG.pdf
[3] Aperio ScanScope Systems. http://www.aperio.com/lifescience/capture
[4] Ir. Frank Vanden Berghen. Small, simple, cross-platform, free and fast C++ XML Parser.
http://www.applied-mathematics.net/tools/xmlParser.html
[5] Markus Buehren. Functions for the rectangular assignment problem, Matlab central file exchange,
2004. http://www.mathworks.com/matlabcentral/fileexchange/6543
[6]
Cancer
mortality
for
all
cancers
combined.
Cancer
Research
UK.
http://www.cancerresearchuk.org/cancer-info/cancerstats/mortality/all-cancerscombined/newpagetemp/
[7]
How
many
different
types
of
cancer
are
there?
Cancer
Research
UK.
http://www.cancerresearchuk.org/cancer-help/about-cancer/cancer-questions/how-many-differenttypes-of-cancer-are-there/
[8] Canny J. A Computational Approach to Edge Detection, IEEE Trans. Pattern Analysis and
Machine Intelligence, Vol.8(6), pages 679–698, 1986.
[9] Subhasis Chaudhuri, Shankar Chatterjee, Norman Katz, Mark Nelson, Michael Goldbaum.
Detection of Blood Vessels in Retinal Images Using Two-Dimensional Matched Filters. IEEE
Transactions on Medical Imaging, Vol.8, No. 3, pages 263-269, 1989.
[10] Michael B. Dillencourt, Hannan Samet, Markku Tamminen. A general approach to connectedcomponent labelling for arbitrary image representations. Journal of the ACM, Vol.39 Issue 2, pages
253-280, 1992.
53
2013
Nikolaos Tzoannou
[11] Gonzalez, R. C., R. E. Woods, S. L. Eddins. Digital Image Processing Using MATLAB, New
Jersey, Pearson Prentice Hall, 2004.
[12] Erika Check Hayden. Cutting off cancer's supply lines. Nature International weekly journal of
science, 458, pages 686-687, 2009 .
[13] Lynn Hlatky, Philip Hahnfeldt and Judah Folkman. Clinical Application of Antiangiogenic
Therapy: Microvessel Density, What It Does and Doesn't Tell Us. Journal of the National Cancer
Institute, Vol. 94, Issue 12, pages 883-893, 2002.
[14] Introduction to morphology operations on images. Computer vision talks, http://computer-visiontalks.com/2011/02/introduction-to-morphology-operations-on-images/
[15] Jahne B. Practical handbook on image processing for scientific applications CRC Press, Boca
Raton, Florida, pages 76-82, 1997.
[16] Label connected components in 2-D binary image. Matlab documentation centre,
http://www.mathworks.co.uk/help/images/ref/bwlabel.html
[17] Craig Larman , Victor R. Basili. Iterative and Incremental Development: A Brief History. IEEE
Computer Society, Vol.36, Issue 6, pages 47-56, 2003.
[18] Chris van der Loos. User Protocol: Practical Guide to Multiple Staining. University of
Amsterdam,
Academic
Medical
Centre.
http://www.biotechniques.com/multimedia/archive/00074/CRI-FP-Microscopy_74545a.pdf
[19] G. S. Ng, R. W. Zhou, C Quek. A Novel Single Pass Thinning Algorithm. Journal Pattern
Recognition Letters, Vol.16 Issue 12, pages 1267-1275, 1995.
[20] Y Nieto, J Woods, F Nawaz, A Baron, R B Jones, E J Shpall and S Nawaz. Prognostic analysis
of tumour angiogenesis, determined by microvessel density and expression of vascular endothelial
growth factor, in high-risk primary breast cancer patients treated with high-dose chemotherapy.
British Journal of Cancer, Vol.97, pages 391-397, 2007.
[21] Mark S. Nixon, Alberto S. Aguado. Feature Extraction and Image Processing, page 88,
Academic Press, 2008.
[22] Nobuyuki Otsu, A threshold selection method from gray-level histograms. IEEE Trans. Sys.,
Man., Cyber. 9 (1), pages 62–66, 1979.
54
2013
Nikolaos Tzoannou
[23] Routine Mayer's Hematoxylin and Eosin Stain (H&E), Manual of Histologic Staining Methods of
the Armed Forces Institute of Pathology (Third Edition). American Registry of Pathology ( Luna, Lee
G., HT(ASCP) (editor)), New York, 1960.
[24] Arnout Ruifrok, Dennis Johnston. Quantification of histochemical staining by color
deconvolution. Analytical and Quantitative Cytology and Histology, Vol.23, Issue 4, 2001.
[25] P. Sahs. DAB: An Advancement in Blood Print Detection. Journal of Forensic Identification,
Vol.42, No. 5, page 412, 1992.
[26] Shapiro, L. G., Stockman, G. C. Computer Vision, pages 137, 150. Prentence Hall, 2001.
[27] Milan Sonka, Vaclav Hlavac, Roger Boyle. Image Processing: Analysis and Machine Vision
(Third edition), 2008.
[28] R. Tyrrell Rockafellar, Roger J-B Wets. Variational Analysis, page 117, Springer-Verlag, 2005.
[29] Roland T. Ullrich, Jan F. Jikeli, Michael Diedenhofen, Philipp Böhm-Sturm, Maike Unruh,
Stefan Vollmar, Mathias Hoehn. In-Vivo Visualization of Tumor Microvessel Density and Response
to Anti-Angiogenic Treatment by High Resolution MRI in Mice. PLoS ONE 6(5): e19592,
doi:10.1371/journal.pone.0019592, 2011.
[30] Umbaugh Scot E. Computer Vision and Image Processing, Prentice Hall, NJ, 1998.
[31] Bruce R. Zetter. Angiogenesis and Tumor Metastasis. Harvard Medical School, Boston, 1998
http://www2.uah.es/farmamol/Public/AnnReviews/PDF/Medicine/Angiogenesis_metastasis.pdf
55
2013
Nikolaos Tzoannou
Appendix A
Personal Reflection
This project was the most challenging part of my degree studies. The piece of work I had to produce
required endless hours of effort and the lack of any prior experience in delivering large scale projects
like this makes the whole process much harder. Through this process I learnt that the successful
delivery is result of a complicated combination of several factors.
First of all, it is important to choose a project that really interests you. This means that you need to
research thoroughly and very carefully every aspect of the project before taking the final decision.
Since, it is a very long process, keeping yourself motivated to work and stay in schedule is very
crucial.
Secondly, you should follow the guidance provided by your supervisor. As mentioned before, final
year project students are totally inexperienced in projects like this. Your supervisor is there to provide
guidance, help and advice. Without his crucial contribution, it would be almost impossible to
complete the project successfully.
Time management is equally important as keeping yourself motivated during the project’s lifetime.
These two factors are related. Keeping yourself motivated most likely will keep you in schedule. But
again, due to the lack of experience, even if you work constantly during these months, you might find
yourself stuck in a particular problem, spending more time than you should on a specific process.
Consequently, you will spend less time on processes which could be more important.
The most significant lesson I learnt is that this project is not only about designing and producing a
solution to a problem. In my point of view, the most important aspect of the final year project is the
learning experience that provides you. More specifically, an extensive background reading and
research is needed in order to be able to understand the problem completely and be able to produce a
solution. But the learning experience is continuous throughout the project’s lifetime. You need to
experiment and apply in practice everything you learnt. Moreover, the need for redesigning and re56
2013
Nikolaos Tzoannou
implementing which most likely will occur at least once, extends this learning experience until the
very end of the project.
In addition to the specific knowledge in a certain field like computer vision, learning is extended to
more general skills as well. During a project such as this one, you learn how to be methodical and
consistent, and also you improve your presentation and writing skills. You learn how to break down a
large piece of work and organise your time and also how to follow guidelines provided by your
supervisor. All these skills are crucial and necessary for our future employability, for any work
environment, regardless the actual nature and field of the job.
Overall, it was a learning experience for me in all these aspects described above, providing both field
specific knowledge and general skills which is part of the successful outcome of the project. If you
haven’t learnt anything during this project, then you did something completely wrong. A personal
way to evaluate the outcome of such a project as a final year student is to ask yourself after the
completion, “What have I learnt?” The more answers you get, the more successful was the project.
57
2013
Nikolaos Tzoannou
Appendix B
External material used
B.1
Colour
deconvolution
Matlab
mex
c
function
written
by
Tom
Macura.
http://svn.openmicroscopy.org.uk/svn/ome/trunk/src/matlab/OME/Filters/colour_deconvolution/colou
r_deconvolution.c
B.2
Implementation of the Hungarian algorithm in Matlab written by Markus Buehren.
http://www.mathworks.com/matlabcentral/fileexchange/6543
B.3
XML
parser
written
in
C
by
Frank
mathematics.net/tools/xmlParser.html
58
Vanden
Berghen.
http://www.applied-
2013
Nikolaos Tzoannou
Appendix C
Detection Evidence
Figure C.1: Successful blood vessel detection for samples a (top left), b (top right), c (bottom left), d
(bottom right)
59
2013
Nikolaos Tzoannou
Figure C.2: Successful blood vessel detection for samples e (top left), f (top right), g (middle left), h
(middle right), i (bottom left), j (bottom right)
60