INTERNATIONAL JOURNAL OF ADVANCED ELECTRONICS & COMMUNICATION SYSTEMS
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Blood Cell Segmentation – A Review
1
Kantilal P. Rane, 1Gauri D. Zope, 2Atul Rane
1
Dept. of E&TC, GF’s GCOE, Jalgaon
2
DGM, L & T, U.S.A.
Emails: [email protected], [email protected]
Abstract- Now a days, preliminary diagnosis of most of diseases
involves analysis of blood or bone marrow smear. Hence it
becomes indispensable to have a scrupulous methodology to
analyze the blood smear or bone marrow smear for the accurate
diagnosis of diseases. Analysis of blood smear involves two types
of checking as, complete blood count (CBC) and differential
blood count (DBC ). In complete blood count the numbers of
Erythrocytes (Red Blood Cells), Leukocytes (White Blood Cells),
and Thrombocytes (Platelets) in blood are accounted to obtain a
concentration of cells per unit volume. In differential blood
count the differential classes of leukocytes in blood are counted
to provide a more detailed diagnosis. However, determination of
constituent parts of blood cells through a manual process arises
susceptible errors due to the different morphological features of
the cells. Hence, there is a need of automation for detection
different blood cells. The scope of this report includes several
such automation processes for detection and classification of
leukocytes only.
I.
(c)
(f)
(g)
Fig. 1. Blood cell components (a) erythrocyte, (b) eosinophil, (c)
lymphocyte, (d) basophil, (e) monocyte, (f) platelet, (g) band neutrophil
INTRODUCTION
Blood circulating in human body is ensemble result of
plasma and cells. Cells include Erythrocytes (RBCs),
Leukocytes (WBCs) and platelets[12]. [26] Leukocytes are
further classified in to granulocytes and mononuclear cells.
(a)
(e)
(b)
(d)
The % of blood cell count has a specific range, so any
abnormalities in cell count or in cell shape or cell size will
result in some disorders[16],[15] like,
1. Acute lymphoblastic leukemia (increased leukocyte)
2. Acute myelocytic leukemia (elevated leukocyte)
3. Chronic granulocytic leukemia
4. Chronic lyphocytic leukemia
5. Hairy cell leukemia
6. Infectious mononucleosis
7. Maleria
8. Sickle cell anemia
Hence, CBC and differential blood count plays a vital role
in hematology. For the hematologist, subjective interpretation
of blood smear become more difficult due to some ambiguity
of cells, or traditional methods. [20], [5]Assessment of blood
smear also suffers from tiredness and capability of
performing repetitive task of observing through microscope.
Also, [23]the manual methods are time consuming. May one
of these reason attracted researches towards computer aided
procedures. Computer aided analysis of blood cells improves
objectivity and reproducibility. Automated methodologies of
detection and classification of leukocytes involve several
image processing steps, such as image acquisition, image
filtering, image enhancing, image segmentation, features
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extraction, classification using different classifiers. Out of
these all steps a scrupulous segmentation will lead to the
accurate diagnosis[8],[29]. Using segmentation techniques,
specific cells are isolated from rest of blood component.
Hence, many researchers are attracted towards segmentation
techniques from few decades.
IMAGE SEGMENTATION
II.
In the field of computer vision, images plays a vital role of
carrying information. Information gathered in images, can be
used for various tasks like, identifying the abnormalities in
blood cells or tissues,identifying cancerous cells, diagnosing
diseases, navigation of robots, in mechanical industries and
many more. Hence, to use the image information in various
application ,it becomes mandatory to extract such useful
information very properly.For extracting proper information
form an image, image segmentation is the fundamental
process. Image segmentation without loosing any information
is also a challenging task in compute vision
applications.Image segmentation, is a process of partitioning
an image in to multiple segments which can make image
analysis more easy. Image segmentation is mainly depends
on detecting discontinuities and detecting continuities.
Segmentation depending upon abrupt changes in intensity
values falls in to category of detecting discontinuities, e.g
edge detection. And method detecting continuities segments
an image in to such a region ,which satisfy the predefine
criteria of simarities e.g region growing ,clustering.
Some of segmentation techniques develoed by researchers
for segmenting leukocytes from rest of blood smear and its
own components as nucleus region and cytoplasm region are
listed below.
III.
REVIEW WORK
Cell segmentation involves removal background, which
includes red blood cells, platelets and other objects mixing in
microscopic images. Cell segmentation become challenging
due to occlusion of cells, illumination inconsistencies,
variation of contrast between cell boundary and background.
Various automated segmentation methods have been
developed, which can be categorized as, edge
detection,threshold based, region-based approach, model
based methods, fuzzy clustering based approach and many
others.
IV.
EDGE DETECTION
Edge detection algorithm,emulates the edge
sensitive
cells capability of human visual system (HVS) [1]. Edges
indicates abrupt changes in intensity levels and this
discontinuity is used for isolating two distinct objects. When
there is a compact stacks of cells around lymphocyte,
membrane thickness becomes very thin,in such case canny
edge detection algorithm can properly segment the membrane
from sub image containing lymphocyte[2]. Also canny based
filters[3] yields output with continuous edges. Edge follower
technique [5] eliminates short coming arises from ill defined
nucleus boundary, by correlating the boundary with points of
maximal local gradient in density distribution.
[27] develops three edge detection algorithms to increase
cell detection probabilities. These algorithms are developed
with inner and outer contour algorithms. In some cases,
threshold value for segmentation is not selected properly
from histogram of original image.[9] developes teager energy
operator. This in combination with teager filter is use in order
to highlight the edges of nucleus. One of advantage of this
technique is that,it can segment nuclei even at low % of
impulse noise.
V.
THRESHOLDING
Thresholding is based on pixel distribution (intensity
based). It is simple but powerful approach for segmenting
light object on dark background[3]. In this, a multilevel
image is converted in to binary image by choosing proper
threshold value T. If the pixel value >> T, then that pixel
belong to object region otherwise the pixel belong to
background region. This can divide object pixel into several
regions and isolate the object from background.
[4] separates leukocyte from background and also,
segments nucleus within it by finding histogram of the
density values having three peaks corresponding to
background, cytoplasm, nucleus. In such a histogram, the
density value corresponding to two local minima are used as
threshold values for boundary tracing. Proposed method
contain drawbacks as, it do not exploit topological features of
nucleus like its shape and number of lobes it contain. The
segment between suitable pair of concavity is replaced by
straight line using the points of maximal concavity in
boundary[5] ,which eliminates problem of touching red cells
to leukocytes under consideration in[4]. Thresholding on the
histogram of smoothed subtracted image is used for
extracting nucleus from rest of background[6]. After isolation
of nucleated cell, a thresholding and morphological
operations are used for extracting nucleus boundary and
smoothing extracted boundary respectively. It can handle
touching cell problem at good extent but some errors occurs
due to irregular shape of nucleus and cells of atypical
cytoplasm contour. Also, this method doesn’t remove some
regions belonging to red blood cells and platelets. Thresholds
may be selected manually or automatically. A recursive
method derived form maximizing interclass variance between
dark, gray, and bright regions[7]. An optimal thresholding is
used for separating dark, gray, and bright regions
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corresponding to nucleus, background, and cytoplasm with
red blood cells respectively. Morphological operations are
used for smoothing the segmented regions. [8] use
thresholding method with morphological operations for
initially labeling of pixels to roughly isolate background, red
blood cells, cytoplasm, and nucleus. It can use colors,
brightness, and gradients information for thresholding
purpose. Since no spatial information is used during threshold
selection, it can make use of shape detection method based on
regional context information for further adjustment of pixel
labels, in order to produce more meaningful results. Instead
of using linear histogram [10] use circular histogram in HIS
color space to improve thresholding results. For thresholding
purpose maximum value of inter-variance of two parts is
used. Also, HIS color contains most of white blood cell
information.
VI.
REGION GROWING
In thresholding and edge detection approach every pixel is
treated independently. He owever, region growing approach
checks connectivity among pixels, in order to decide whether
these pixels belong to same region (object) or not. Mainly
these algorithms use predefined similarity criteria to group
pixel in to large region, known as homogeneity criteria. It
starts from randomly selecting a single pixel (seed pixel) and
region is grown around it, until resulting region satisfy a
homogeneity criteria.
Local color similarity and global spatial cohesion
combinely used as a criteria of homogeneity to aggregate the
pixels [11]. [12] presents a comparison of region growing
process on RGB and L*a*b color space. For this comparison
color homogeneity criteria is used such as, it consider white
blood cell image as a set of region which changes the color
abruptly when it cross the boundary between regions.
VII.
CLUSTERING
Clustering is also based on similarities between a pixels.
After defining similarity between pixels, similar pixels are
grouped together to form a cluster on the basis of the
principle of maximizing the intra an inter class similarity.
Clustering is mainly used when the classes which
corresponds to clusters are already know[13]. A novel
technique for WBC segmentation is proposed is proposed in
[14] which combines scale-space filtering and watershed
clustering. It can avoid variety and complexity in image
space. Here, clustering is applied on sub image containing
WBC. Scale-space filtering is used for isolating nucleus
region and cytoplasmic region is isolated by using watershed
clustering. It can use HSV color space instead of RGB color
space due
to its low correlation. An automation in
differential counting is achieved using k-means clustering
followed by EM algorithm [15]. A nucleus is first located
using k-means clustering. On the sub-image containing WBC
with localized nuclei, undergoes to k-means clustering
followed by EM algorithm to get final segmented image. The
advantage of system is , it doesn’t require user interaction or
parameter tunning. Fuzzy c-means clustering is used to
generate the patches [16] in cell images. These patches are
then combine to form three segments as, nucleus, cytoplasm,
and background, but this requires overly segmented image for
generating patches. The problem of scattering and false
clustering due to unclear and color pixel similarity between
cytoplasm and plasma background is eliminated
by
modifying FCM clustering[17]. An iteration of false
scattering color replacement by using a neighboring color
data is used for modification purpose. The modification
improves U membership matrix by allowing FCM clustering
again and again iterately. Fuzzy c means clustering on
reduced dimension color space (L*a*b) is used to form four
clusters corresponding to RBCs, WBC nucleus, cytoplasm,
background [18]. Each pixel in L*a*b color space is classifed
into any of four clusters by calculating the Euclidean distance
between each pixel and mean value. Also mean value of
randomly selected sample region in a*b color space is used to
avoid overlapping problem. K-means clustering on H
component and on S component of HIS color space yields
segmented WBC from background and segmented WBC
nucleus respectively [19]. [20] a kernel induced new metric is
used instead of Euclidean norm in FCM, for modifying the
FCM clustering. It can reduce number of iterations and
doesn’t requires reinitialization and automation.
VIII.
CONTOUR
Contour are deformable curves, they can be deform to
object boundaries in images. These curves can move due to
internal forces within the curve itself and due to external
forces derived from the image data. These forces are depends
on how the curve conforms to object boundary or other
desired features within image.
Hence, these curves are also known as deformable
contour, active contour or snakes[21].
Highly robust and efficient approach is depicted in[22] for
cell segmentation using seed point and contour model. An
approach, first find the seed point by an efficient , iterative,
non-parametric clustering algorithm which indicate
approximate central nuclei position. Then deformable
contours are expanded for finding nucleus boundary. Though
it handle problem of overlapping and closely packed nuclei, it
doesn’t work on simple plateau nucleus profile since it
doesn’t define initial criteria for flat maxima. On sub-image
containing WBC with localized nucleus initial round shaped
contour is placed at the center of nucleus inside WBC [23].
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For deformation of initially placed contour active contour
algorithm is applied to gradient flow vectored image. Once
this contour reaches equilibrium shape (shape of WBC),
WBC can easily isolated form smear. But wrong initial
position and size of contour may lead to incorrect
segmentation. [24] utilizes parametric contour model for
segmenting the WBC nuclei. After obtaining clear edges of
nucleus boundary, Gradient Vector Flow (GVF) is used as
internal and external forces to guide snakes to deform to
nucleus boundary edges. Segmented nucleus is then
subtracted from WBC sub image to obtain cytoplasm region.
WBC nucleus has significant large Cb component value in
comparison with RBCs and WBC cytoplasm[25]. This
advantage YCbCr color space in combination with active
contour produces correct WBC segmentation result even in
absence of WBC nucleus.
IX.
color space describes color more consistently with human
eye.
Method
Edge
Detection
Advantages
Energy
minimization
procedure,
Emulate ability of
human visual system
Disadvantages
Poor performance
on images having
unsharp boundaries
and scattered edges.
Limited
performance in case
of
real
world
images.
Thresholdi
ng
Simple,
computationally less
expensive,
no need of priori
information
Suffers due to nonuniform background
illumination,
No
spatial
information is used,
hence can’t produce
more
meaningful
information,
noise sensitive
Region
Growing
More immune to
noise than edge
detection
Not very stable,
Results are sensitive
to choice of local
uniformity
predicate,
Number of seeds
provided by user
may
not
be
sufficient to assign
every pixel to a
region
Cluster
Easy
implementation
to
Mostly number of
clusters are not
known,
No use of spatial
information,
Which criteria is
used
for
better
segmentation is not
known
Active
contour
Deformable contour
can change their
shape and size
Preserve global line
shape
Poor accuracy for
unclear
object
boundary
Watershed
transform
Improves
capture
range, yields closed
contours unlike edge
detection
Over segmentation
SOME OTHER APPROACHES
The combination of RGB and HSV color space forms a
nucleus Leukocyte Nucleus Enhancer (LNE) [26]. It enhance
leukocyte nucleus and suppress rest of blood component.
Also, for RBCs, the difference between S component of HSV
color space and G component of RGB color space is small
while in case of leukocyte nucleus this difference becomes
very large. And this property is used for separating RBCs and
leukocyte nucleus.
Patches (set of connected pixels) and relationship amongst
them provide more useful and meaningful information ,
hence [28] utilizes patches instead of single pixel. The
patches are generated by watershed transform method where
each primitive patch is no bigger than one of cell component
(nucleus, cytoplasm, RBC, background). These patches are
then labeled either nucleus, cytoplasm, RBC or background
using fuzzy relaxation method. Fuzzy relaxation method
make fuzzy and probabilistic classification result at every
point in each iteration and adjusting these decisions at each
successive iteration based on local context information. But it
can make incorrect labeling ,if too bright illumination may
lead to bright gaps in cytoplasm, [8] defeated this drawback
by using shape detection method based on large regional
context information. A watershed transformation by IFT
with thresholing also yield better segmentation result[29],and
leaking problem is also eliminated by regularizing contours
using scale-space toggle operator. Extending previous work
[30] develop multiscale approach using self dual multiscale
morphological toggle operator. In this, the information
obtained from different representation levels is used for
localizing interest features in original image. A new approach
includes color histogram based and region growing-merging
based segmentation method [31] which utilizes HIS color
space, since Hue, Saturation, Luminance component of HIS
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X.
CONCLUSION
In this paper, we discuss main segmentation techniques
used for leukocyte segmentation in microscopic blood smear
images. It can seen that, in spite of half a century down road,
still a perfect segmentation technique is not achieved for cell
image segmentation. It may due to the fact that,
computerizing the capacity of human with which he can see
the things in image with an ease is notoriously difficult task
XI.
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
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