INSTITUTO SUPERIOR TÉCNICO
INTRODUÇÃO À ENGENHARIA BIOMÉDICA
TUTORED BY PROF. MARGARIDA SILVEIRA
2011-2012
CLASSIFICATION
OF SKIN LESIONS
IN DERMOSCOPY
IMAGES
Ana Filipa Raimundo nº73248
Ricardo Trindade nº73654
Júlia Pinheiro nº73696
Jorge Martins nº 73615
Instituto Superior Técnico
Classification of Skin Lesions in Dermoscopy Images
Index
Abstract ......................................................................................................................................... 3
Introduction .................................................................................................................................. 4
Existing Methods ........................................................................................................................... 5

ABCD rule .......................................................................................................................... 6

7-Point Checklist................................................................................................................ 8
Motivation for automated algorithms .......................................................................................... 9
Automated Algorithms .................................................................................................................. 9

Segmentation .................................................................................................................. 10

Feature Extraction ........................................................................................................... 13

Classification.................................................................................................................... 15
Example of automated algorithms .............................................................................................. 16
Ongoing Investigation ................................................................................................................. 20
Conclusion ................................................................................................................................... 21
Bibliographic References ............................................................................................................. 22
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Classification of Skin Lesions in Dermoscopy Images
Abstract
Melanoma is a deadly proliferation of melanocytes that has the potential to metastasize and in
order to prevent its deadly consequences and for treatment to be efficient it is necessary to
perform and early diagnostic of the melanoma.
The diagnostic can be performed without any support, in the naked eye, although the result
isn’t always reliable, therefore dermoscopy was created. It consists in using a device to take a
picture of the lesion in order to analyze its features to determine whether the lesion is benign
or not.
As some people don’t have access to a dermatologist, and even with an experienced eye the
result can be false, it is necessary to develop automatic methods in order to increase the
accuracy of the diagnostic.
These automatic methods consist in three stages, segmentation of the image, to isolate the
lesion from the rest of the skin, feature extraction, and classification, to decide the malignancy
of the lesion.
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Introduction
Melanoma is a malignant proliferation of melanocytes that has the potential to
metastasize. It is the deadliest kind of skin cancer and its incidence has increased
significantly over the last decades throughout the world.
Melanomas with metastasis are usually resistant to the current treatments. Therefore,
the chance of healing is related to the tumor excision in the early stages of
development. Thereby, having a fast and reliable method that allows a premature
diagnosis it’s of extreme importance.[1]
Many times it is impossible to a physician to diagnose a pigmented lesion through its
characteristics, even if it is an experienced professional, in the naked eye. For this,
additional criteria are necessary for a clinical diagnosis.
Thereby, the opportunity to create a support method that permits the increasing of
precision in the pigmented skin lesions arises: dermoscopy is created.
With this non-invasive technique, also known as dermatoscopy or epiluminescence
microscopy, we are able to diagnose skin lesions such as melanomas and other
pigmented lesions.
Dermoscopy is essentially a support method for physicians allowing a diagnosis based
in much more information that only with the naked eye and clinical experience. It is
also known as dermatoscopy, incident light microscopy, skin surface microscopy or
epiluminescence microscopy since it uses epiluminescence microscopy (ELM) images (a
non-invasive technique, in vivo) in order to have more detailed images of the lesion
and also to visualize subcutaneous structures or morphologies that otherwise wouldn’t
be taken into account – and that can be extremely important in a correct diagnosis.
It uses a dermatoscope (some digital imaging instruments are already being used but
the dermatoscope is still the main option) that will “inspect” the lesion after mineral
oil, alcohol or water (a fluid with the main purpose of eliminate the light reflections
and create translucent properties in the skin) had been placed in the lesion.
A
B
Fig. 1 - Dermoscopy images
A –The use of a dermatoscope; B,C – Skin lesion
C
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Classification of Skin Lesions in Dermoscopy Images
Nowadays physicians use various methods to analyze and diagnose skin pigmented
lesions from dermoscopy images, such as ABCD rule and 7-point checklist. However
these algorithms present some disadvantages and lack of liability. For example, the
application can be very subjective depending on the experience of the physician.[2]
Due to the difficulty and subjectivity of human interpretation, computerized analysis of
dermoscopy images has become an important research area.
It is relevant to denote that this tool does not aim to substitute the physician, but to
help general practitioners improving the liability of the diagnosis.
Our project consists in researching the development of dermoscopy. We hope to give
an overall look of the processes used in these systems and emphasize the importance
of its contribution to an earlier and more accurate diagnosis of melanoma.
In this report we will try to approach the theme by a technical point of view and also
refer the importance and the contribution that these methods can have in improving
the medicine practice.
Existing Methods
 Non-automatic
Nowadays they are various algorithms of dermatologic lesions analysis without any
automated systems. The older one is Pattern Analysis (first described by Perhamberger
in 1987). This particular method consists in the observation of the lesion evaluating if
its characteristics resemble to a melanocytic or non-melanocytic lesion.
For benign melanocytic lesions we have features like:




Reticular Pattern (diffuse network)(1)
Homogeneous Pattern (2)
Comma-like regular vasculature (3)
Symmetrical Blotches (4)
(1)
(2)
(3)
(4)
Fig. 2 – Features of benign melanocytic lesions
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Classification of Skin Lesions in Dermoscopy Images
In melanomas we have some features like:
 Multicomponent pattern(4)
 Irregular Blotches(5)
 Blue-Whitish Veil(6)
(4)
(5)
(6)
Fig. 3 – Features of melanoma
Following that, specific criteria (or Pattern) of each lesion are used to obtain a
diagnosis.
Although widely used, this method has some reliability issues due to its qualitative
characteristics.
Therefore, other algorithms of dermatologic analysis were introduced: ABCD rule and
7-point Checklist. Both of them are only applied after the lesion is classified as
melanocytical.
 ABCD rule
The ABCD rule is a semi-quantitative analysis of four characteristics considered by
Stolz, in 1994, as the most crucial for melanoma diagnosis. To the previous
classification as melanocytical or non-melanocytical Stolz suggests an altered version
of the algorithm proposed by Keusch and Rassner, in 1991.
The characteristics evaluated by the ABCD rule are asymmetry (A), border (B), color (C)
and differential Structure (D) and after that a score is attributed.[2]
To evaluate asymmetry, the lesion is bisected with orthogonal axes positioned in a way
to assure the minor score of asymmetry possible. If signs of asymmetry are visible in
both axes, the score given is 2, if these signs are visible just in one of the axes, the
score is 1. Otherwise the score is 0. Usually melanomas have asymmetries classified
with 2. It is important to refer that natural irregularities are expected because hardly
any lesion will be perfectly symmetrical.
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In the case of border evaluation the lesion is divided in eight equal parts. For each one
of these parts that present an abrupt change in pigmentation in the border receives 1
point. Therefore, in this parameter the score can assume values between 0 and 8
points.
Regarding the color evaluation, six shades are taken in consideration: red, light-brown,
dark-brown, dark-blue, black and white (only if it is lighter than the surrounding skin).
For each of these shades found in the lesion a score of 1 point is attributed. For this,
the score lays somewhere between 1 and 6. It is normal to find melanomas with a
score of 3 in the color parameter.
Finally, in differential structures, it’s important to recognize a series of components:
pigmented network, homogeneous areas, streaks, dots, globules, blue-whitish veil,
vascular structures, etc. The homogeneous areas should cover, at least, 10% of the
lesion area. Streaks and dots are only accounted when more than two elements are
clearly visible. As in the globules, only one has to be present to be accounted for.
Therefore, 1 point is attributed for each of these structures present in the lesion. The
score can take values between 5 and 1. In this way, the higher the number of
structures, the higher the probability of the lesion being a melanoma.
At last, to calculate the final score (Total Dermoscopy Score – TDS) the following
formula is used:
TDS = [ (A score x 1.3) + (B score x 0.1) + (C score x 0.5) + (D score x 0.5) ]
After the TDS is calculated the diagnosis is obtained from the following table:
Total Dermoscopy Score (TDS)
Interpretation
<4.75
Benign melanocytic lesion
4.8-5.45
Suspicious lesion; close follow-up or
excision recommended
>5.45
False-positive score (>5.45)
sometimes observed in:
Lesion highly suspicious for melanoma





Reed and Spitz nevi
Clark nevus with globular pattern
Congenital melanocytic nevus
Melanocytic nevus with exophytic
papillary structure
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 7-Point Checklist
This method is based on the analysis of seven dermatologic characteristics typically
found in melanoma. It is considered an improvement comparing with Pattern Analysis
because its application is much simple and efficient allowing less experienced clinicians
to use the model.
The analyzed characteristics in this method are divided in major and minor criteria due
to its significance in the diagnosis.
The three major criteria are atypical pigment network, blue-whitish veil and atypical
vascular pattern. The atypical pigmented network consists in black, brown or gray
network with irregular meshes and thick lines. In the other hand, the blue-whitish veil
are Confluent, gray-blue to whitish-blue diffuse pigmentation associated with pigment
network alterations and other morphological structures as dots, globules and/or
streaks. At last, the atypical Vascular Pattern is linear, irregular or dotted vessels not
clearly combined with regression structures and associated with pigment network
alterations, dots/globules and/or streaks.
A
B
C
Fig. 4 – Major Criteria of 7-Point-Checklist
A –Atypical pigment Network; B –Blue Whitish Veil; C- Atypical Vascular pattern
When analyzing the lesion about its major criteria, a score of 2 is given when any of
these characteristics are shown and 0 if they’re absent.
Regarding minor criteria, which are irregular streaks, irregular pigmentation, irregular
dots/globules and regression structures. The irregular streak is a, more or less
confluent, linear structure not clearly combined with pigmented network lines. The
irregular pigmentation is black, brown and/or gray pigmented areas with irregular
shape and/or distribution. The irregular dots/globules are black, brown and/or gray
round to oval, variously sized structures irregularly distributed within the lesion. At
last, the regression structures are white and blue areas, scar-like appearance, which
may be associated. The presence of each one of this features is classified with 1 point
and the absent with 0 points.
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A
B
C
D
Fig. 5- Minor Criteria of 7-point checklist
A-Irregular Streaks; B- Irregular Pigmentation; C-Irregular Dots/Globules; D- Regression
Structures
Finally, to calculate the final score, all the points attributed to the characteristics are
added and the result is classified as nevus if the score is less than 3 and as melanoma if
the score is equal or superior to 3.
Motivation for automated algorithms
Although all of these methods have a good trust rate, there is also some inconvenience
in their appliance. For instance, there is always some subjectivity in the parameters
evaluation and its consequent lesion diagnosis. In addition, in some countries there are
few dermatologists so people are examined by regular physicians, untrained in dermatology.
Besides it, all these algorithms take a lot of time to be applied in each lesion by the
practitioner. So it is not viable to apply them in a large scale. Therefore, a solution for
this matter was needed. In the end, the goal was to create an objective, reliable and
fast diagnostic method. To achieve this goal the research for efficient automated
algorithms started.
Automated Algorithms
This methods use computerized systems to recognize and analyze a dermoscopy image
of a lesion, considering its features and providing a diagnosis. As there are various forms
and colors present in skin lesions it is necessary that the diagnostic algorithm takes into
account these variables.
All the algorithms have three stages:



Segmentation
Feature extraction
Classification.
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Classification of Skin Lesions in Dermoscopy Images
 Segmentation
In computerized diagnosis of skin lesions, the first step is the segmentation of the
image. In this fundamental step, the area of the lesion is isolated from the rest of the
image in order to permit an easier observation and diagnosis of the lesion.
To use this technique, several methods have been implemented, all using algorithms
or differential equations in order to segment the image in an efficient manner.
Before approaching the topic of the methods developed, it’s necessary to talk about
preprocessing the image. In this step (before the actual segmentation), the image is
enhanced in two separate ways: by improving the resolution of the image and by
removing the hair that may block the image. The first part consists in using contrast
enhancement (histogram equalization that changes pixels to achieve a uniform
distribution) and noise reduction techniques to improve the resolution of the image,
that’s usually poor illuminated and noisy. The second step is achieved by erosion of the
image with a line segment that removes all straight lines perpendicular to it.
Therefore, using two perpendicular straight lines to erode the image, all hairs are
removed from it.
We can also apply a median filter to the image to ensure that any unwanted structures
are eliminated.[8]
A
B
Fig. 6 – Preprocessing Image
A –Original Image; B –Enhanced lesion
Now, moving to the segmentation topic, one of the most used techniques is the active
contours or snakes. This technique consists in “deforming a curve toward the
minimization of a given energy (…) composed by two terms, one attracting the curve to
the objects boundaries, and the other one addressing regularization properties of the
deforming curve.” In other words, this method draws an initial curve that will start to
deform until it reaches a contour in the image (which, in the best case scenario, is the
border of the lesion).
There are two main methods to utilize this technique, which are Geodesic Active
Contours Approach and Geodesic Edge Tracing. The first one is based on “starting from
a closed curve, either inside or outside the object, deforming it toward the (possibly
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Classification of Skin Lesions in Dermoscopy Images
local) minima, finding a geodesic curve” (a path of minimal weighted distance)[5].
Although this method has the advantage of being fully automatic and of being
adaptable to color images, it doesn’t work well on noisy images, and if the boundary in
the object is weak, it may not detect it.
Therefore, the second method was developed, which is based on “connecting a few
points marked by the user on the object boundary, while keeping the weighted length
to a minimum.” [4]Although this method is useful in handling noisy images, it’s not
fully automatic, always needing the intervention of the user to mark the initial points.
A
D
B
F
C
G
Fig. 7 – Segmentation techniques
A,B,C,D – Geodesic Active Contours Approach; E,F – Geodesic Edge Tracing
Another segmentation technique already studied is the “Adaptive segmentation of
gray areas”, which focus on the brightness component of the skin image, that carries
most of the information on the color gray. In this case, using a mask previously
sketched by the dermatologist, the melanoma area (along with the region of interest
[ROI]) is selected from the entire image. We can notice that most of the times, the ROI
covers the brightness values in the brightness histogram, but using segmentation with
just this data may generate a falsely positive area. Therefore, we need to select the
values in the brightness histogram that interest in the identification of the lesion’s ROI.
The solution is by calculating the different gray areas in the lesion and their ratio with
the total area of the lesion. Finally, we need to find a range of values that are shared
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Classification of Skin Lesions in Dermoscopy Images
by the gray areas and by the ratio. After treating all this data in a statistical fashion, we
found our ROI, thus completing the segmentation of the image.[5]
A
B
Fig. 8 – Segmentation using “Adaptive segmentation of gray areas”
A – Skin Lesion; B-Brightness plane of A; C –Brightness histogram of A
C
On the other hand, to segment images with a smooth transition between the lesion
and the skin, one useful option is the Level set segmentation, that is based in adapting
a curve existent between the lesion and the skin region, thus delineating the area of
the lesion in an efficient manner, and adapting the curve to the region of the lesion
(instead of just the border, like in the geodesic methods)[6]. This method was
compared with two similar ones, Robust Snakes and Active Thresholding. The first one
is based on “estimating the object contour using elastic models in the presence of
cluttered background i.e., some of the features extracted from the image (e.g., edge
points) should not attract the model”. Essentially, this method finds line segments in
the image (since they’re more reliable than edge points) and then makes an estimative
in order to fit an elastic curve between the chosen segments. This technique needs
interaction with the user, since he must mark two points in the image, one in the skin
region and another in the lesion, so that the computer may measure the intensity of
both areas and define the line segments from that data.
Finally, Active Thresholding is a technique that evaluates the color of every pixel in the
image and compares it with a Threshold (usually the blue RGB component). Then, if
the color of the pixel is darker than the Threshold, the pixel is considered active, thus
part of the lesion. The resulting binary image is then processed to fill any exiting hole
and to select the largest connected component of the image. It’s noteworthy to
mention that the Threshold varies with each image in order to adapt to each skin
lesion’s color.
When compared with each other, we can conclude that Active Thresholding, due to its
simplicity, may result in very good or very bad outcome, while the Robust Snakes, even
though it doesn’t have so many good outcomes, it’s more reliable because it has very
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few bad outcomes. The Level Set method was the least impressive, but may be
improved by using different probability density functions.
The results, however, show that visually all three methods are very similar, and though
they’re all efficient choices when it comes to automatic segmentation, the choice with
best results is still manual segmentation, even if it has the disadvantages of being
highly time consuming and very expensive.
 Feature Extraction
After the segmentation stage, it is necessary to analyze the image and recognize
morphological structures that can indicate melanoma. These structures are basically
the same ones that non automated algorithms look for. As in segmentation, there are
various ways to extract the features of the image.
Probably the simplest ones consist in applying formulas to the image to look for
specific features like dots or blue-whitish veil, due to its high correlation with
melanoma[7].
A
B
C
Fig. 9 - Feature extraction
A –Spotting dots on a lesion; B, C – Finding a blue-whitish veil in a lesion
Another of the methods consists in a probabilistic classifier. There is an online
database called Dermis where some features were extracted like border irregularity,
area index, standard deviation from mean roundness and Heywood circularity index.
Then the classifier is trained with this data and applies it to the actual lesion. This is
done by using Bayes’ conditioned probability, according to a feature vector x and a
class w1-melanoma or nevus w2, P(wi|x), according to the following formula.
As this is a probability model there are some errors associated to it, like assigning a
melanoma to a benign lesion or vice-versa. To correct this errors a loss function is
implemented.
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The most complex methods of feature extraction analyze various components like
shape features, color features and texture features.
In shape features the total area of the lesion and its compactness are calculated.
Calculating the area of the lesion can be done by counting the number of pixels inside
the border. However, this method is not very accurate for objects with rough borders.
For this reason, the lesion area is usually calculated using the method of bit quads[3]
which has been shown to be one of the most accurate area estimators. In the case of
compactness, it is usually defined as the ratio of the area of the object to the area of a
circle with the same perimeter. This measure compares the object with a circle, which
is the most compact shape. However, this requires accurate estimation of the object
perimeter. Therefore, an alternative version that avoids using the perimeter is
calculated as the ratio between the equivalent and maximum diameters. Other shape
features include maximum lesion diameter (the maximum distance between two
points on the border), eccentricity (a measure of elongation), solidity (a measure of
border irregularity), and two measures related with the object-oriented bounding box
(the smallest rectangle that contains the lesion and is aligned with the principal axes):
rectangularity (the ratio between the areas of the object and object-oriented bounding
box) and elongation (ratio between the height and width of the object orientedbounding box).
In color features, various parameters are taken into account like color asymmetry,
centroidal distances, LUV histograms and more. Color asymmetry is a measure of the
pigment, centroidal distances is defined as the distance between the geometric
centroid and the brightness centroid. LUV histogram it’s used to determine the color
similarity of two regions.
Fig. 10 – Feature extraction (analyzing asymmetry)
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 Classification
In this last stage, like in the other two, there are various ways to proceed. One of the
most used and probably simpler is K-Nearest Neighbor (KNN). The key idea behind
KNN classification is that similar observations belong to similar classes. Thus, one
simply has to look for the class designators of a certain number of the nearest
neighbors and weigh their class numbers to assign a class number to the unknown.
The weighing scheme of the class numbers is often a majority rule, but other schemes
are conceivable. The number of the nearest neighbors, k, should be odd in order to
avoid ties, and it should be kept small, since a large k tends to create misclassifications
unless the individual classes are well-separated.
It can be shown that the performance of a KNN classifier is always at least half of the
best possible classifier.
Fig. 11 – Representation of a possible application of KNN
Another common method is Support Vector Machine (SVM). This process basically
analyzes data and recognizes patterns used for classification and regression analysis.
Given a set of training examples, each marked as belonging to one of two categories,
an SVM training algorithm builds a model that assigns new examples into one category
or the other. An SVM model is a representation of the examples as points in space,
mapped so that the examples of the separate categories are divided by a clear gap that
is as wide as possible. New examples are then mapped into that same space and
predicted to belong to a category based on which side of the gap they fall on.
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Fig. 12 - Schematic representation of Support Vector Machine Classification
Even if the lesion is classified as benign, it is important to keep the evolution under
control. To do so, there are also automated mechanisms that compare two images of
the same lesion at different moments.[9]
A
B
C
Fig. 13 – Evolution of a lesion
A and B – The lesion at two different time points; C – Changes that occurred using the
difference image
Example of automated algorithms
In the following we will describe a system for inspecting pigmented skin lesions and
melanoma diagnosis that also includes a decision support component which combines
the outcome of the image classification with context knowledge such as skin type, age,
gender,
and
affected
body
part.
The segmentation stage is based on a Thresholding method, which has a small defect
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of including on the segmented image darker areas. To correct this mistake the darker
areas of the image (with low spatial frequency) are removed.
A
B
C
Fig. 14 – Dermoscopy images of skin lesions
A- Illumination-Compensated image; B- Typically input image; C-Erroneous
segmentation results (red contour) caused by uneven background illumination
After this a 3D-plot is created with luminance levels and image position, and the
computed average area of luminance is the lesion area, i.e. the lesion area is between
B and C.
Fig. 15 – 3-D Plot allowing the calculation of the lesion area
After segmenting, the system applies the ABCD-rule as the way to extract the features
and a score is obtained. In this method, a lesion its considered suspicious when the
total score is superior to 4.75.[2]
As classification, the algorithm proposes a probabilistic approach, where an algorithm
computes the probability of being a melanoma or not according to the person’s
context i.e. skin type, age, gender and affected body part, to decide whether someone
is in the risk group or not.
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Fig. 16 – Melanoma incidence by part of the body and gender
If the probability computed for melanoma is high, then a classifier assigns the features
to a melanoma group or not. After that, the lesion ‘is plotted’ in a normal distribution:
if the total number of features is superior to a threshold then we have a melanoma.
The accuracy of this classifier was 86% with 94% sensitivity. Furthermore, the results
have shown that including patient data is beneficial for diagnosis, although the patient
data could be extended to improve the ratings.
Some studies try to make a correspondence between the location of a primary
melanoma in a patient and the location of lymph nodes, allowing to better
understanding of lymphatic drainage and at the same time an earlier diagnosis of
melanoma since the detection of melanoma cells in lymph nodes is "one of the earliest
signs that cancer has spread" and if there were a primary lymph nodes location sentinel nodes (SN) - corresponding to the location of the melanoma to analyze - with
sentinel lymph node biopsy (SLNB) - instead of "general" nodes the diagnosis would
be quicker.
In order to enable this lymph location it was created a "lymphatic map" using
lymphoscintigraphy (LSG) imaging, injecting a radioactive tracer and following its track
as it drains to the SN's. But this LSG imaging has shown that melanoma does not
spread in a clinically predictable manner. So The Sydney Melanoma Unit (SMU) created
a database in order to relate several information of more than five thousand patients
and
help
in
a
better
diagnosis
of
melanoma.
Fig. 17 - Lymphoscintigraphy (LSG) imaging
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Initially it was created a 3D skin model from a cloud of data, result of digitization of 2D
images. The initial "mesh" is several times readjusted to create the most reliable
model in order to be compared and assembled to the lymph node model. Lymph
nodes are "small bean-shaped structures" located in a large number in groin, armpit
and abdominal areas. Areas where lymph nodes are generally located are called
"lymph node fields". Some smaller lymph nodes were located in the model according
to blood vessels and other anatomical structures described in literature. With the
model created the data from SMU was integrated in it with "SN locations for each
melanoma patient being recorded as a code according to the node field it was located
in". Projection of the node fields was not made directly since the spatial information
has not been recorded in a generic manner.
Fig.18 – Location of lymph nodes
"Spatial statistical analysis of the data has been conducted, investigating melanoma
sites based on sentinel lymph node fields and vice versa. Fields have been fitted on the
skin model to visualize the likelihood that a primary melanoma site on the skin will
drain to a particular node field." A great result from this study is an ability to select skin
segments and visualize which are the potential draining lymph node fields with
percentage of likelihood (calculated from previous cases). A problem in this model is
the absence of the neck and head information but it is one of the future objectives in
the continuous construction of this model.
The final step of this model - it's integration in clinical environment - will enable
doctors to have a more detailed information about each case (specially enabling a
detailed knowledge of where to analyze in order to have an earlier correct diagnosis of
melanoma) fighting harder against melanoma.[10]
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Ongoing Investigation
ADDI - Automated computer-based diagnosis system for dermoscopy images
This project is a partnership between University of Porto, University of Aveiro, Instituto
Superior Técnico, Institute of Systems and Robotics and the Pedro Hispano Hospital HPH (Matosinhos). It combines several experienced professionals -doctors and
academic researchers - in dermoscopy, tele-dermoscopy and telemedicine at the HPH
and the necessity of a new and more effective "image analysis system for dermoscopic
images".
The Hospital database - that counts with 4000 cases but at this point not in a practical
or reliable automatic way for clinical use - will be changed in its format and enhanced
with new information. In the end of this process some representative cases will be
chosen to evaluation being analyzed and its detailed scoring registered in the database
by trained volunteers at HPH.
In order to create a robust automated system it's extremely necessary that the
algorithm used allow that the great diversity in dermoscopic images and undesired
features (as noise) won´t be a problem to the correct segmentation of the skin lesion
made by many segmentation methods that will be "compared with the strong and
weak points identified".
An important step is the development of algorithms used in the feature extraction
from the lesion image, then incorporate it in the system that also needs to distinguish
malignant images and to present a tentative classification. Feature extraction will be
made in two different ways: one attempting to apply the kind of classification and
variables used by doctors and another using mathematical features.
Obviously it would be necessary to test and evaluate the system in real applications
and as soon as "beta version of the software is available, it will be installed at the
HPH". At this point it will be used in clinical practice serving its purpose in diagnosing
skin lesions and at the same time finding which are its "limitations".
The bases for this project assent on a software with low intervention from doctors and
a capacity of showing all the "little steps" since the image reception until the final
diagnosis (segmentation of the lesion, features values extracted, tentative of
classification). It will provide a useful tool for doctors in order to help them in a more
correct diagnosis, and a good way for "education and training purposes" (initialize
some doctors with dermoscopy, for example) together with the data collected with the
system.
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Classification of Skin Lesions in Dermoscopy Images
Conclusion
Even though this report was based in a less practical approach to the subject, meaning
that it was essentially related to bibliographic research, we still were able to withdraw
some conclusions about the automated methods for diagnosing skin lesions and their
applications.
The main one was that it is still a work in progress, being that even though there are
already a lot of satisfying methods available (as analyzed in previous topics), none of
them is perfect and they all have flaws, so there are still ongoing investigations trying
to improve each approach to make it the more efficient in its ground of application.
We can also conclude that these methods have a wide range of applications in the
medical word, since they reduce the time and cost of a diagnosis and can be very
helpful to experienced dermatologists, selecting the cases that need a manual
observation from the rest (in the case of classification methods) or isolating the lesion
from the rest of the skin to permit a closer and more accurate observation (in the case
of segmentation methods).
Finally, from the range of techniques we researched and analyzed, we can also believe
that the manual diagnosis is still the best option, but since is not efficient enough (cost
and time-wise), the best alternative is to select a few of the automatic methods, so
they can adapt to each situation, using each one when it seems to fit the purpose
better, instead of using a single method (with all its flaws) for all the images, which
would decrease the efficiency of the process.
Therefore, if their application is supervised by an experienced user and if they are used
according to their most high-skilled capacities (alternating with other techniques
according to the purpose), we conclude that the methods of automated diagnosis for
skin lesions available for usage are efficient and a balanced replacement for the
manual diagnosis.
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Classification of Skin Lesions in Dermoscopy Images
Bibliographic References
Websites:
http://www.roche.pt/sitestematicos/infocancro/index.cfm/tipos/melanoma/?gclid=CIbShYni7awCFZNX4QodphosAg
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34
http://www.scielo.br/pdf/abd/v81n3/v81n03a09.pdf
http://www.dermoscopy.org/atlas/base.htm
http://www2.fc.up.pt/addi/
http://emedicine.medscape.com/article/1130783-overview#showall
Scientific articles:
. [1] Alessandro Parolin, Eduardo Herzer, Cláudio R. Jung “Semi-Automated Diagnosis
of Melanoma Trough the Analysis of Dermatological Images” , Conference on graphics,
Patterns and Images, Vol. No ,pp 71-78, 2010
. [2] José Fernández Alcón, Calina Ciuhu, Warner ten Kate, Adrienne Heinrich, Natallia
Uzunbajakava, Gertruud Krekels, Denny Siem, Gerard de Haan “Automatic Imaging
System with Decision Support for Inspection of Pigmented Skin Lesions and Melanoma
Diagnosis”,IEEE Journal of selected topics in signal processing, Vol.3, No.1, pp1425,February 2009
. [3] M. Emre Celebi, Hassan A. Kingravi, Bakhtiyar Uddin, Hitoshi Iyatomi, Y. Alp
Aslandogan, William V. Stoecker, Randy H. Moss – “A methodological approach to the
classification of dermoscopy images”, Computerized Medical Imaging and Graphics,
Vol.31 pp. 362-373, 2007
. [4] Do Hyun Chung and Guillermo Sapiro “Segmenting Skin Lesions with PartialDifferential-Equations-Based Image Processing Algorithms” -, IEEE Transactions on
Medical Imaging, Vol. 19, No 7, pp. 763-767, July 2000
. [5] - Gianluca Sforza, Giovanna Castellano, R. Joe Stanley, William V. Stoecker, Jason
Hagerty , “Adaptive Segmentation of Gray Areas in Dermoscopy Images”
. [6] Margarida Silveira, Jorge S. Marques, “Level Set Segmentation of Dermoscopy
Images” –http://www.dermoscopy.org, 2000
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Classification of Skin Lesions in Dermoscopy Images
.[7] M. Emre Celebi, Hassan A. Kingravi, Y. Alp Aslandogan, William V. Stoecker,
“Detection of Blue-White Veil Areas in Dermoscopy Images Using Machine Learning
Techniques” – SPIE Vol.6144
.[8] - Omid Sarrafzade, Mohammad Hossein Miran Baygi, Pejhman Ghassemi, “Skin
Lesion Detection in Dermoscopy Images Using Wavelet Transform and Morphology
Operations”, 17th Iranian Conference of Biomedical Engineering, November 2010
.[9] Stein Olav Skrøvseth, Thomas R. Schopf, Kevin Thon, Maciel Zortea, Marc Geilhufe,
Kajsa Møllersen, Herbert M. Kirchesch and Fred Godtliebsen,
“A Computer Aided Diagnostic System for Malignant Melanomas”, - IEEE 2010
[10]. Hayley M. Reynolds, P. Rod Dunbar, R. F. Uren, and Nicolas P. Smith,"Computer Modeling
Provides a New Tool for Clinically Diagnosing Melanoma Spread through the Lymphatics",
Proceedings of the 28th IEEE, EMBS Annual International Conference, pp. 5307 - 5310, New
York City, USA, Aug 30-Sept 3, 2006.
. Hitoshi Iyatomi, M.Emre Celebi, Gerald Schaefer and Masaru Tanaka, "Automated Color
Normalization For Dermoscopy Images", Proceedings of 2010 IEEE 17th International
Conference on Image Processing, pp. 4357 - 4360, September 26-29, 2010, Hong Kong.
. Jerzy W. Grzymala-Busse Zdzislaw S. Hippe, "Data Mining Methods Supporting Diagnosis of
Melanoma", Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
(CBMS’05), 2005
Teresa Mendonça, André R. S. Marçal, Angela Vieira, Jacinto C. Nascimento, Margarida Silveira,
Jorge S. Marques, Jorge Rozeira, "Comparison of Segmentation Methods for Automatic
Diagnosis of Dermoscopy Images",Proceedings of the 29th Annual International, Conference of
the IEEE EMBS, Cité Internationale, Lyon, France, pp. 6572 - 6575, August 23-26, 2007.
. Harald Ganster, Axel Pinz, Reinhard Röhrer, Ernst Wildling, Michael Binder, and Harald
Kittler,"Automated Melanoma Recognition", IEEE Transactions On Medical Imaging, Vol. 20,
No. 3,pp. 233 - 239, March 2001
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