50120140501007 2-3

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 1, January (2014), pp. 52-61
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com

IJCET
©IAEME

A NOVEL METHOD FOR CLUMPED PARTICLES SEPARATION IN
MICROSCOPIC IMAGES
A. AL-Marakeby
Systems and Computers Engineering Dept., Faculty of Engineering, Al-AzharUniversity,
Cairo, Egypt

ABSTRACT
Microscopic image processing has been recently applied to many fields such as blood cell
counting, tissue analysis and material microstructures analysis. Cell counting is an important process
which helps the diagnosis of many diseases. A main problem in cell counting and also in other
microscopic image analysis is the overlap between objects. This overlap and connection between
particles reduces the accuracy of counting or gives classifications errors. Many techniques have been
used to isolate the objects but the segmentation process still has many errors. In this research,a novel
method for separation of clumped particle is developed. This method depends on iterative hypothesis
and verification technique. Extracted features are used to generate a set of hypotheses, depending on
particles boundary and colors. These hypotheses are verified using specific measures and distances,
and then the best hypothesis is chosen. This method is efficient for generic shape analysis and
matchinginstead of the assumption of circular or elliptical particles shapes.In addition to that, the
proposed technique overcomes the problems of noisy and cut boundary, and the problems of
computational complexity in some other techniques. This method is compared to circle and ellipse
detection methods and higher accuracy is achieved.
Keywords: Particles Separation- Segmentation-Microscopic Images – Blood Cell Counting.
1. INTRODUCTION
Segmentation is very important stage for most successful image processing and computer
vision applications. The errors in the segmentation are diffused to the next stages and cause the low
performance of the final results. Microscopic image processing is a field concerns with the analysis
and processing of images obtained from a microscope. Microstructures analysis of material,
complete blood count (CBC), and tissue analysis are some examples from many applications of
microscopic image processing. The automation of blood cells counting has the advantages of
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International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME
reducing costs and increasing speed and precision. The correct segmentation of blood cells represents
a challenge when the cells are overlapped. Connected component analysis is a simple technique used
to detect cells, but it gives correct results only when the cells are far and separated from each other's.
Fig.1 shows some of connected cells which are in interpreted as a single cell. Venkatalakshmi et.al.
used Hough transform to detect and count red blood cell[13]. Hough Transform has better
performance than connected component analysis but still suffer from many problems. Cell shape can
be non-circular , and some overlaps structures are difficultto be detected by Hough transform.

Fig.1 Overlapped Cells
Sharif et. al. used masking and watershed algorithm to segment RBC[6]. Huang used
watershed segmentation combined with morphological operators to separate overlapping blood
cells[5]. Theerapattanakul et al. used active contours for the segmentation process of blood cells[7].
Other techniques based on SVM, neural networks, and Gabor filter, can be found in [8][9][11][14].In
this research a novel method is developed which solves many problems found in other techniques.
This method depends on iterative hypothesis and verification technique. Extracted features are used
to generate a set of hypotheses, depending on particles boundary and colors. These hypotheses are
verified using specific measures and distances, and then the best hypothesis is chosen. This method
is efficient for generic shape analysis and matchinginstead of the assumption of circular or elliptical
particles shapes. This paper is organized as follow: section 2 explains the circle and ellipse detection,
section 3 explains the proposed technique working with generic shape detection, section 4 illustrates
the color model used in the separation process , section 5 gives the results and discussions, and
finally section 6 gives the conclusion.
2. CIRCLE AND ELLIPSE DETECTION
An edge detection stage is required before running the shape detection algorithms. Edge
detection converts the color or gray image into binary image, where the boundaries of objects are
detected. There are many edge detection algorithms such as Sobel, Prewitt, Roberts and Laplacain,
Canny edge detection. In this research Sobel edge detection is used as shown in fig.2.Compared to
other edge operator, Sobel has two main advantages: 1- Since the introduction of the average factor,it
has some smoothing effect to the random noise of the image. 2-Because it is the differential of two
rows or two columns, so the elements of the edge on both sides has been enhanced,so that the edge
seems thick and bright[15].

53


Fig. 2 Sobel edge detection
From many circle and ellipse detection techniques, Hough Transform (HT) is used to detect
particles. The main advantage of the HT in extracting circle and ellipse, is its robustness against
discontinuous or missing data points. This is becausethe HT does not require the connectivity of all
the contour pointsof circle or ellipse [1].
2.1 Circle detection
HT converts features points in the 2Dimage into parameter space. HT for circle detection
depends on the detection of 3 parameters: the circle center coordinates a,b and the radius r. The
circle equation is given by:
ሺ‫ ݔ‬െ ܽሻଶ ൅ ሺ‫ ݕ‬െ ܽሻଶ ൌ ‫ ݎ‬ଶ

(1)

This equation can be represented in parametric space as:
x = a + r * cos(θ), y = b + r*sin(θ)

(2)

The main problem with HT for circle detection is the computational complexity and memory
requirements. Many techniques are used to reduce the complexity and memory requirements of HT.
Randomized Hough Transform (RHT)keeps standard Hough Transform (SHT)'s robustness,but costs
much less memory space and computing time than SHT.[4] [12]. RHT each time samples 3 edge
points of the image randomly and calculates 3 parameters (center position and radius) of the circle to
be detected instead of mapping all theedge points to parameter space in SHT [4].Gradient methods
are used to restrict the center of the circle and also the radius to some value extract from the gradient
of the curves. Xing et. al. used one-dimensional circle Hough transform depending on that the circle
center is on the gradient line of circle edge points[16]. Fig.3 shows using HT to separate particles in
microscopic images

Fig.3 Separation of particles based on HT circle detection.
54

2.2 Ellipse detection
Ellipse is a more general shape model than circle model. For this system many particles have
elongated shapes which make the circle HT is not accurate. The problems in circle detections are
increased when moving to ellipse detection. While circle detection depends on 3 parameters, ellipse
detection has 5 parameters. Fig.4 shows the different parameters required for specifying an ellipse.
These parameters are the center of the ellipse, the major axes, the minor axes and the angle of the
ellipse with X axes. The following equation represents the parametric equation of the ellipse:
(3)
Evidently this approach would require solving the equation for five different pointson the
ellipse, thus mapping xy points to a five dimensional space, (hence having to manage a
fivedimensional accumulator). This approach is not only memory expensive but also computationally
intensive, asthe algorithm in its brute search form would have
complexity[2]. As in circle
detection, many algorithms are used also in ellipse detection to reduce its complexity. Randomized
Hough Transform (RHT) is used in ellipse detection, where an n-tuple ofpixels in the image is
mapped to a single point in the parameterspace[10].Han et al. simplified ellipse detection by
dividing the image into several sub-images by the properties of ellipse, andthen point pairs are
chosen from the sub-images to calculate the parameters of ellipses[3]. Alex et al. considered every
pair of edge pixels as possible end points of the major axis of a hypothetical ellipse. After that, all
other edge pixels will be used to vote on the half-length of the minor axis of this hypothetical
ellipse[1]. Fig.5 illustrates the separation of particles using HT for ellipse detection.

L2
L1

O

Fig.4 ellipse parameters

Fig.5 Separation of particles based on HT ellipse detection.
3. GENERIC SHAPE DETECTION SYSTEM
The circular and elliptical assumption for particles shapes is not valid for all particles. Many
particles have deformed shapes especially when they are touched or connected with other
particles.These non-circular and non-elliptical shapes reduce the accumulator value for this object.
Object detection depends on thresholding the accumulator values where value less than kis not
considered as an object.Hence, to detect these objects, a threshold for parameter space should be
55

decreased, causing the detection of false objects. Fig.6 shows these problems where the shapes of the
particles are not exactly fit to the ellipse or circle assumption.

Fig.6 non-circular and non-elliptical shapes
The technique used in this research depends on generic shape analysis and matching. The
boundaries of these shapes are extracted from different images and stored in a database. The
distances between the particle under inspection and all shapes in the database are measured. An
iteration process starts with the generation of assumptions and verification of these assumptions. The
assumptions covers different shapes exists in the database, different dimensions and coordinates, and
the overlap of particles (single particle, two particles, three particles …….etc). The verification of
assumptions depends on an evaluation function which has terms for many factors.
3.1 Boundary Extractionand Shapes database
Many shape descriptors exists such as Fourier descriptors, wavelet descriptors, signatures,
moments,…etc. Most of these descriptors depend on the closed contours. Noisy contours with cut
regions or concatenated objects cause problems for these techniques. In other words the Fourier
descriptor of a special boundary is completely different with the Fourier descriptor of the half of
this boundary. Hence, in this research the spatial domain is better and easier in working with it. We
can conclude that this shape consists of two concatenated particles by a simple analysis. The
boundaries of different particles with different shapes are extracted as shown in fig.7. The shapes are
selected to avoid similar shapes. These boundaries are stored in a database. The database contains the
full resolution boundaries and different resolutions or modified boundaries can be obtained after that
by sampling or filters.

Fig.7 boundary extraction

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3.2 Boundary Distance
The distance measure between the boundary of the particle under inspection and a stored
template in the database is important for obtaining good performance. The small value of this value
indicates better interpretation and robust results. The large value indicates unknown or uncertain
interpretation and hence increases segmentation and analysis errors. The distance between template
and boundary is measured by the summation of all distance from each point in the boundary to the
template. The template is approximated by a set of line segments. Each segment linear equation is
extracted and the distance between a point and a line is given by equation 4. The solid line in fig.8
represents the template while the dotted line represents the boundary of the particle.

ࢊൌ

|ࢇ࢞ା࢈࢟ାࢉ|
ඥࢇ૛ ା࢈૛

(4)

Fig.8 boundary distance
3.3 Hypothesize-and-test framework
A process of generating assumptions and testing if these assumptions are valid or not, is used
to separate the particles and determine the location, shape, and dimensions of these particles. The
search space of this process is very large and many techniques are used to find the solution
efficiently with high accuracy. The search space include finding the overlap type (no overlap – two
particle – three -….etc), the location of centers and the shape of particles. After each assumption
generation the verification process is applied and the distance is measured as discussed in the
previous section. To reduce the search space with affecting the accuracy, the boundary color
analysis, gradient analysis, and maximum curvature analysis are used. Color Analysis is detailed and
illustrated in section 4. The gradient analysis of the boundary is very important and reduces the
search space too much. The center of the particle is deduced from the curvature of the boundary, and
hence no need to test other assumptions. The center may be not accurate but searching around the
determined location gives very accurate results. The maximum curvature analysis detects some
points which are candidate to be the connection of two or more particles. Some noise and deviated
boundaries may generate false points but the verification stage will discard any false assumptions.
Fig. 9 illustrates the assumption generation for one and two particles. Fig.10 shows the maximum
curvature points (blue dots).

57


Fig.9 assumptions and verifications

Fig.10 maximum curvature points (blue dots)
The verification stage depends on many factors. The main factor is the boundary distance
discussed in section 3.2. This distance has a clear discrimination between single or multiple particles.
The other factors used in generating hypotheses are used again to measure and verify the assumption.
The color model and the maximum curvature points are used but with a smaller weight while these
factors are not dominant and may give false results. Some other heuristics are used to accelerate the
process of hypotheses generation and also in the verification of these assumptions. Fig.11 shows the
segmentation of three clumped particles.

Fig. 11 segmentation of three overlapped particles

58

4. COLOR MODEL
Working with edges and boundaries only, discards a lot of useful information which can be
important for solving the overlap problem. The particles color model has a significant impact on the
performance of the system. As shown in fig.6 the color and intensity are varied overthe area of the
particles. A light spot can be found near the center of the particle, and a dark region exists at the
overlapping area. In Fig.12the color analysis for blood cell image is illustrated.

Figure 12: Color and intensity variations over particles
This analysis is based on different thresholds for colors and each level or cluster is assigned a
specific color. The result shows that two connected particles has two spots while each single particle
has a unique spot. This analysis is not usually true and it is used for generating hypotheses. The
iterative process for hypothesize and test can verify or discard the assumptions generated by the
color model as discussed in section 3.
5. RESULTS AND DISCUSSIONS
A set of blood cell images is used to test the developed system [17]. Four techniques are
used: 1- connected component analysis, HT for circles, HT for ellipse, and the generic shape
detection discussed in section 3. Table.1 reports the accuracies of these techniques.
Table.1 Accuracy of different techniques for counting blood cells
Technique
Accuracy
Connected component Analysis

88%

Hough Transform for circle detection

92%

Hough Transform for Ellipse detection

94%

Generic Shape

98%

The connected component analysis has no separation at all, so any clumped particles are
interpreted as a single particle. HT for circle can separate particles but it is restricted to the circular
shapes and sometimes generates false object detection. The HT elliptical system is similar to circle
detection but with more generalization to elliptical shapes. The proposed technique of generic shape
detection system has many improvements and robustness. It can detect more shapes, deals with noise
and cut boundary, and has higher accuracy.
59

6. CONCLUSION
Separation of clumped particles represents a problem for obtaining better results for
microscopic image analysis. The assumption of particles shapes to be standard shapes such as circles
and ellipses is not valid assumption and causes errors and reduced performance. The proposed
technique in this research has better performance and deals with different shapes effectively. Using
color information to separate overlapped particles increases the accuracy and improves the
performance. The hypothesize and test framework used in this research reduces the search space
based on bottom up features interpretation and some other heuristics.
REFERENCES
[1]

[2]
[3]
[4]

[5]
[6]

[7]

[8]

[9]

[10]
[11]

[12]

[13]

Alex Yong, Sang Chia, Maylor K. H. Leung, How-Lung Eng, SusantoRahardja, ELLIPSE
DETECTION WITH HOUGH TRANSFORM IN ONE DIMENSIONAL PARAMETRIC
SPACE. IEEE International Conference on Image Processing, 2007.
Cosmin A. Basca, MihaiTalos, and Remus Brad, Randomized Hough Transform for Ellipse
Detection with Result Clustering, Serbia & Montenegro, Belgrade, November 22-24, 2005.
Han Fei, GuoYanling, Wang Lili, A New Ellipse Detector Based on Hough Transform,
Second International Conference on Information and Computing Science2009.
Huadong Sun, Yaobin Mao, Ningjian Yang, Dian Zhu,A Real-Time and Robust Multi-Circle
Detection Method Based on Randomized Hough Transform, International Conference on
Computer Science and Information Processing (CSIP) 2012.
HUANG Jiandeng, An Improved Algorithm of Overlapping Cell Division, International
Conference on Intelligent Computing and Integrated Systems (ICISS), 2010
J. M. Sharif, M. F. Miswan, M. A. Ngadi, MdSahHj, Salam, MuhammadMahadi bin Abdul
Jamil,Red Blood Cell Segmentation Using Masking and Watershed Algorithm: A
Preliminary Study,2012 International Conference on Biomedical Engineering (ICoBE), 2012,
J. Theerapattanakul, J. Plodpai, C. Pintavirooj, AN EFFICIENT METHOD FOR
SEGMENTATION
STEP
OF
AUTOMATED
WHITE
BLOOD
CELL
CLASSIFICATIONS,. IEEE Conference TENCON 2004.
JiyeQian, Bin Fang, Chunyan Li and Lin Chen, Coarse-to-Fine Particle Segmentation in
Microscopic Urinary Images, 3rd International Conference onBioinformatics and Biomedical
Engineering , 2009.
Nasution, AMT, EK Sury`aningtyas, Comparison of Red Blood Cells Counting using two
Algorithms: Connected Component Labeling and Back projection of Artificial Neural
Network, IEEE IPGC 2008.
Robert A.McLaughlin, Randomized Hough Transform: Better Ellipse Detection , IEEE
TENCON - Digital Signal Processing Applications, 1996
R. Safabakhsh, F. Zamani,A Robust Multi-Orientation Gabor Based System for
Discriminating Touching White and Red Cells in Microscopic Blood Image, Information and
Communication Technologies, 2006.
SI-YU GUO, XU-FANG ZHANG, FAN ZHANG, ADAPTIVE RANDOMIZED HOUGH
TRANSFORM FOR CIRCLE DETECTION USING MOVING WINDOW, Proceedings of
the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16
August 2006
Venkatalakshmi.B1, Thilagavathi.K, Automatic Red Blood Cell Counting Using Hough
Transform, Proceedings of 2013 IEEE Conference on Information and Communication
Technologies 2013.

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[14] Wei-Liang Tai1, Rouh-Mei Hu2,5, Han C.W. Hsiao, Rong-Ming Chen, and Jeffrey J. P. Tsai,
Blood Cell Image Classification Based on Hierarchical SVM, IEEE International Symposium
on Multimedia, 2011.
[15] WenshuoGao, Lei Yang, An Improved Sobel Edge Detection, 3rd IEEE International
Conference on Computer Science and Information Technology (ICCSIT), 2010
[16] Xing Chen, Ling Lu, Yang Gao, A New Concentric Circle Detection Method Based onHough
Transform, The 7th International Conference on Computer Science & Education (ICCSE)
2012.
[17] http://library.med.utah.edu/ (2013).

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