Seediscussions,stats,andauthorprofilesforthispublicationat:http://www.researchgate.net/publication/263036817

SelectionofthebestfeaturesforleukocytesclassificationinbloodsmearmicroscopicimagesARTICLEinPROCEEDINGSOFSPIE-THEINTERNATIONALSOCIETYFOROPTICALENGINEERINGFEBRUARY2014ImpactFactor:0.2DOI:10.1117/12.2043605

DOWNLOADS149

VIEWS114

4AUTHORS,INCLUDING:

OmidSarrafzadehIsfahanUniversityofMedicalSciences6PUBLICATIONS3CITATIONS

SEEPROFILE

HosseinRabbaniIsfahanUniversityofMedicalSciences104PUBLICATIONS309CITATIONS

SEEPROFILE

ArdeshirTalebiIsfahanUniversityofMedicalSciences69PUBLICATIONS361CITATIONS

SEEPROFILE

Availablefrom:HosseinRabbaniRetrievedon:22July2015

SELECTION THE BEST FEATURES FOR LEUKOCYTES

CLASSIFICATION IN BLOOD SMEAR MICROSCOPIC IMAGES

Omid Sarrafzadeh*a, Hossein Rabbani

a, Ardeshir Talebi

b, Hossein Yousefi-Banaem

a

aDept. of Biomedical Engineering, Faculty of Advanced Medical Technology, Isfahan University of

Medical Sciences, Isfahan, Iran; bDept. of Pathology, School of Medicine, Isfahan University of

Medical Sciences, Isfahan, Iran

ABSTRACT

Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment

of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and

classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that

pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of

nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have

singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2

to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and

Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6

geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of

leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results

obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood

cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with

previous literatures.

Keywords: Blood smear microscopic image, feature extraction, fuzzy C-means clustering, leukocytes (WBCs)

segmentation and counting, SVM classifier.

1. INTRODUCTION

Blood consists of three types of cells and cell fragments floating in a liquid called plasma. These cellular components

are: Red Blood Cells ("erythrocytes", "RBCs"), White Blood Cells ("leukocytes", "WBCs") and Platelets. WBCs play a

significant role in the diagnosis of different diseases such as leukemia and different types of infections [1], so extracting

information from them is valuable for hematologists. WBCs composition also reveals important diagnostic information

about patients. Substituting automatically detecting and counting of WBCs for manually locating and counting different

classes of WBCs is an important topic in the domain of cancer diagnosis [2]. Microscopic differential WBC count is still

performed by hematologists, being indispensable in diagnostics with malignance suspicious. This as a reference method

is slow and subjective and its reproducibility is poor, however its value for blood samples containing abnormal cells

remains indisputable. Therefore, automation of this task is very helpful for improving the hematological procedure and

accelerating diagnosis of many diseases [3]. WBCs contain nucleus and cytoplasm and there are five types of leukocytes

(WBCs) found in the blood: Neutrophils, Basophils, Eosinophils, Lymphocytes, and Monocytes. Each cell type has a

specific role to play in our body's immune system [4]. The texture, color, size and morphology of nucleus and cytoplasm

make differences among WBCs. The segmentation step is very crucial because the accuracy of the subsequent feature

extraction and classification depends on the correct segmentation of WBCs. It is also a difficult and challenging problem

due to the complex nature of the cells and uncertainty in the microscopic images. Therefore, this step is the most

important challenge in many works in the literatures and improvement of cell segmentation has been the most common

effort in many researches. Many blood smear image segmentation methods have been proposed in the area of general

segmentation of WBCs which are generally based on edge and border detection, region growing, filtering, mathematical

morphology, and watershed clustering [5-14]. Despite many beneficial explorations have been carried out in WBCs

segmentations, majority of them have some defects such as complexity of arithmetic, difficulty to ensure parameters and

so on. In this paper, the boundary of WBCs in the images is manually determined to decrease the effects of segmentation

errors, but the nucleus and cytoplasm of leukocytes are separated automatically by a fuzzy C-means clustering method.

Also, several researchers have previously proposed features to differentiate leukocytes [15-20]. Although many useful

researches have been done for classification of WBCs, many of them have some defects. Some methods extracted

features only from nucleus which lead to have less correct rate. In some works features are extracted from gray-level

images which do not use rich information that color images have. In some researches features are extracted from the

entire cell (not specific features from nucleus or cytoplasm) which usually causes more error in classification. Many

methods used singly geometric features or texture features. In this paper, first nucleus and cytoplasm are segmented

using fuzzy C-means (FCM) clustering and morphological operations in blood smear microscopic images. Then, proper

features are extracted from nucleus, cytoplasm and the entire cell. Finally, leukocytes are classified using proper features

by SVM classifiers. The outline of the article is as follows. Section two deals with methodology and presents the

proposed algorithm. Results and discussion are presented in section three.

2. METHODOLOGY

First, we briefly introduce different types of WBCs and how to differentiate them in a blood smear microscopic image.

There are five different types of Leukocytes (WBCs). Leukocytes could generally be divided into two groups: 1)

Granulocytes which contain granules (inclusions in their cytoplasm) and usually have lobulated or segmented nucleus.

Neutrophils, Basophils and Eosinophils are in this group; 2) Agranulocytes (Mononuclear) which do not contain granules

and do not have lobulated or segmented nucleus (Mononuclear refers to having one nucleus). Monocytes and

Lymphocytes are in this group. Neutrophils account for the highest amount of WBCs (about 60%). Neutrophils nucleus is divided into 2 to 5 segments and stains dark purple (multi-lobed nucleus). Cytoplasm is pale pink to tan with fine pink-

purple granules and have 12-16 micrometers in diameter. Eosinophils account for about 3% of WBCs in the blood.

Eosinophils nucleus is blue and divided into 2 segments. Cytoplasm is full of pale pink to tan with large orange and red granules and have 14-16 micrometers in diameter. Basophils have lowest number of WBCs in blood (about 1%).

Basophils nucleus has 2 lobes that stains purple and is difficult to see and their cytoplasm is pale pink tan but contains large purple/blue-black granules obscure nucleus and have 14-16 micrometers in diameter. Monocytes comprise about

6% of WBCs in the blood. Monocytes have singular nucleus (convoluted shape); kidney shaped, bean shaped, or

horseshoe shaped with a deep indentation and stains a blue-gray color with ground glass cytoplasm with tiny granules and Vacuoles (cavity filled with fluid in the cytoplasm) are sometimes presented. Monocytes are the largest normal

blood cells and have 14-20 micrometers in diameter. Lymphocytes are second most common WBCs in blood (about

(a) (b) (c (d) (e)

Figure 1. Types of Leukocytes (WBCs) (a) Neutrophil, (b) Eosinophil, (c) Basophil, (d) Monocyte and (e) Lymphocyte.

TABLE 1. Leukocytes (WBCs) specification

WBCs ~% in

blood Nucleus Cytoplasm

Size

(m)

Neutrophils 60% Divided into 2 to 5 segments and stains dark

purple (multi-lobed nucleus). Pale pink to tan with fine pink-purple granules. 12-16

Eosinophils 3% Blue and is divided into 2 segments. Full of pale pink to tan with large orange and

red granules. 14-16

Basophils 1% Have 2 lobes that stains purple and is

difficult to see.

Pale pinktan but contains large purple/blue-black granules obscure nucleus.

14-16

Monocytes 6%

Have singular nucleus (convoluted shape);

kidney shaped, bean shaped, or horseshoe

shaped with a deep indentation.

Stains a blue-gray color with ground glass cytoplasm with tiny granules, Vacuoles are

sometimes present.

14-20

Lymphocytes 30% Have large, round or oval, dark staining

nucleus.

Little to no cytoplasm with pale blue in color.

Occasional purple-reddish granules. 8-15

30%). Lymphocytes have large, round or oval, dark staining nucleus and little to no cytoplasm with pale blue in color.

Lymphocytes occasionally have purple-reddish granules and have 8-15 micrometers in diameter. Table 1 contains brief

specification of WBCs and figure 1 shows different types of leukocytes. Blood smear of 10 patients have been used in

this work. Digital microscopic images of WBCs are captured with Canon V1 camera mounted on Canon optical

microscope. The data are stored in jpg format with 38722592 pixels and 300 dpi resolution. The images are in sRGB

color space. Border of WBCs is manually determined to overcome the error of segmentation, but nucleus and cytoplasm

are separated using fuzzy C-means and morphological operations which is presented in the following.

2.1 Nucleus segmentation

The algorithm for nucleus segmentation is briefly illustrated in figure 2. The input microscopic image stores in three

Red, Green and Blue (RGB) channels. The median filter is applied on each R, G and B band separately to reduce noise

and also not to lose edges. Since the red, green and blue components are highly correlated, it is difficult to execute some

image processing algorithms. So, the image is converted from RGB color space to L*a*b* color space. The L*a*b*

color space (also known as CIE L*a*b* or CIELAB) is colorimetric, perceptually uniform and device independent. The

L*a*b* color space is an excellent decoupler of intensity (represented by lightness L*) and color (represented by a* for

red minus green and b* for green minus blue) [21]. The sRGB and L*a*b* images of WBCs with their subspaces are

shown in figure 3. The colors in a* and b* subspaces are classified using fuzzy C-means clustering to cluster the cells

into two clusters. Since the nucleus is darker than cytoplasm, the cluster with minimum mean of 3 R, G and B colors is

consider as nucleus cluster and the other one as cytoplasm cluster. Some morphological operations are performed on the

nucleus cluster to eliminate probable false segments. First, the morphological open operation (erosion followed by

dilation) is done to smoothes the contour of nuclei, breaks narrow isthmuses and eliminate thin protrusions. For opening

operation, a flat disk-shaped structuring element with radius 2

is used. After opening, the morphological hole filling

operation is applied to fill inside the segmented regions.

Finally, the nucleus segmented part subtract from the entire

cell to obtain cytoplasm part. Figure 4 shows the result of

applying the algorithm on some images in our data base.

2.2 Feature extraction

Features that are extracted categorize in color features,

geometrical features and texture features which are introduced

each one in the following. It should be mentioned that we had

few basophils in our data set and then we disregard classifying

basophils.

2.2.1 Color features

As can be seen in table 1, types of WBCs have less color

difference in nucleus but more in cytoplasm, so a measure of

color from both nucleus and cytoplasm could be valuable.

RGB color space is not proper for extracting color features

because the red, green and blue components are highly

correlated and also is not well suited for describing colors in

terms that are practical for human interpretation. HIS (Hue,

Saturation, Intensity) color space corresponds closely with the

way human describe and interpret color. The HIS model also

has the advantage that it decouples the color and gray-scale

information in an image [21]. From table 1 it is clear that color

of nucleus in WBCs doesnt vary so much and usually contain single chromatic colors, but color of cytoplasm in WBCs vary

especially in agranulocytes due to the existence of granules. So

the median of hue and saturation of both nucleus and

cytoplasm and standard derivation of hue and saturation of just

cytoplasm are considered as color features. So, 6 color features

are considered.

Input sRGB Image

Apply median filter to R,G,B bands

Convert to L*a*b* color space

Select a* & b* components as features

Apply Fuzzy C-means

clustering

Apply morphological

operations on nucleus cluster

Subtract nucleus and extract

cytoplasm

Retrieve the nucleus and

cytoplasm segments from

original RGB image

Figure 2. Steps to segment nucleuses

2.2.2 Geometric features

From specification of leukocytes in table 1, there are noticeable differences for nucleus geometry of leukocytes, so

geometric features should be extracted from nucleus not cytoplasm and are considered as follow. The number of

segments of nucleus, compactness, eccentricity and solidity of nucleus which are defined as follow (for nucleus with

more than one segment, these features are calculated for the biggest segment). The normalized shape compactness

measure to be unity for a circle is given by (1):

2

4

PC

A (1)

where C is the value of shape compactness, A is the shape area and P is the shape perimeter [22]. Eccentricity is used as a

scalar that specifies the eccentricity of the ellipse that has the same second-moments as the region. In this definition,

eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. Solidity is a scalar

Figure 3. From top to bottom: Neutrophil, Eosinophil, Lymphocyte and Monocyte. From left to

right: sRGB, Red band, Green band, Blue band, L*a*b*, L* band, a* band and b* band.

Figure 4. From top to bottom: Neutrophil, Eosinophil, Lymphocyte and Monocyte. From left to

right one after the other: WBC and segmentation of nucleus and cytoplasm.

specifying the proportion of the pixels in the convex hull that are also in the region and is computed by equation (2).

A

SCA

(2)

where S is the value of solidity, A is the shape area and CA (Convex Area) is the area of convex image. As we see from

table 1, the size of WBCs is a crucial parameter, so area of cell and nucleus to cytoplasm area ratio are considered as

further geometric features. Thus 6 geometric features are extracted just from nucleus (except for area of cell and nucleus

to cytoplasm area ratio which used information of both nucleus and cytoplasm). It should be mentioned that the main

geometric features (the number of segments of nucleus, compactness, eccentricity and solidity) are extract just from

nucleus.

2.2.3 Texture features

From table 1 we see that cytoplasms texture varies among the cytoplasm of WBCs, so texture features are extracted only from cytoplasm of leukocytes as follows. Statistical measures of texture such as mean (a measure of average intensity),

standard derivation (a measure of average contrast), smoothness, third moment (the skewness of a histogram), uniformity

and entropy (measure of randomness) [21] are computed. Therefore 6 statistical features are extracted. The expressions

for the nth moment about the mean and other statistical measures are defined in equations (3) through (8):

1

0

Ln

n i i

i

z m p z

(3)

Standard derivation : 2 z (4)

Smoothness : 21 1 1R (5)

Skewness : 1

3

3

0

L

i i

i

z m p z

(6)

Uniformity : 1

2

0

L

i

i

U p z

(7)

Entropy : 1

2

0

logL

i i

i

e p z p z

(8)

Also the moment invariants are computed as follow: The 2D moment of order (p+q) of a digital image f(x,y) is defined

by equation (9).

,p qpqx y

m x y f x y for p,q = 0,1,2, (9)

where the summations are over the values of the spatial coordinates x and y spanning the image. The corresponding

central moment is defined by equation (10).

, ,p q

p q

x y

x x y y f x y (10)

where 10 01

00 00

m mx and y

m m

The normalized central moment of order (p+q) is defined in equation (11).

00

pq

pq

for p,q = 0,1,2, (11)

where 12

p q

for p+q = 2,3, .

A set of 2-D moment invariants that are insensitive to translation, scale change, mirroring, and rotation can be derived

from the equations (12) through (18):

1 20 02 (12)

2 2

2 20 02 114 (13)

2 2

3 30 12 21 033 3 (14)

2 2

4 30 12 21 03 (15)

2 2

5 30 12 30 12 30 12 21 03

2 2

21 03 21 03 30 12 21 03

3 3

3 3

(16)

2 2

6 20 02 30 12 21 03 11 30 12 21 034 (17)

2 2

7 21 03 30 12 30 12 21 03

2 2

12 30 21 03 30 12 21 03

3 3

3 3

(18)

So 7 moment invariants 1 7[ ,..., ] features are extracted. Finally, 25 features are used in order to classify

leukocytes into four groups: Neutrophil, Eosinophil,

Lymphocyte and Monocyte.

2.3 Classification

The main task of the classification part is to classify the

WBCs into the category they belong to. Four categories of

WBCs were selected for recognition in this work (see

figure 5). Four Support Vector Machine (SVM)-based

classifiers with one to one architecture are used for

classification as shown in figure 6. In this architecture, new

type of WBCs (Basophil) needs to be trained individually

without any effect on prior classifiers. SVM classifiers are

independent, which means each one is trained individually

and there are only two possible outputs for each classifier.

For example, SVM1 is learned to detect Neutrophil and

reject other WBCs. All data are fed to each SVM

separately. For cross-validation of each classifier,

randomly half of the data is used for training and the

remaining is kept for testing. A linear kernel function is

used to map the training data into kernel space and a

Sequential Minimal Optimization (SMO) method is used to

Figure 5. Selected categories for WBCs

Figure 6. Classification based on one to one architecture.

Eosinophil

SVM4

SVM3

SVM2

SVM1

All

dat

a

Neutrophil

Lymphocyte

Monocyte

find the separating hyperplane. Correct-rate, sensitivity and specificity are used to evaluate performance of classifiers.

3. RESULTS AND DISCUSSION

The total numbers of images which are used in this research are mentioned in Table 2. Twenty five features are extracted

from each WBC in order to classify cells into four groups. Each classifier is trained for 100 times and in each training

procedure, randomly half of the data is considered as training set and the classifier is evaluated by residual half of the

data as testing set. The results of evaluation are presented by three parameters. Correct rate (Correctly Classified

Samples / Classified Samples), Sensitivity (Correctly Classified Positive Samples / True Positive Samples) and

Specificity (Correctly Classified Negative Samples / True Negative Samples) for each classifier are shown in table 2.

Our work is compared with two recent similar works. Features introduced in [20] are considered and we name them

Veluchamy features. Veluchamy features are 27: Geometrical features (Area, Perimeter, Circularity, Form-factor, K (No

of pixels)); First order statistical features (Mean, Dispersion, Variance, Average Energy, Skewness, Kurtosis, Median,

Mode); Second order statistical features (Energy, Inertia, Entropy, Homogeneity, Max probability, Inverse difference,

Correlation) and algebraic moment invariants ( 1 7[ ,..., ] ). Our algorithm has been implemented with no changes

except for feautures. We have extracted Veluchamy features from the entire cell as Veluchamy et al did. The results of

classification for Veluchamy features are shown in table 3. Other features introduced in [13] are considered for

comparison and is named Mohapatra features. Mohapatra features are: Fractal dimension; Contour signature; Shape

features (Area, Compactness, Solidity, Eccentricity, Elongation, Form-factor); Color features (the mean color values in

RGB and HSV color spaces); Texture features (Homogeneity, Energy, Correlation, Entropy). We have extracted

Mohapatra features from the nucleus as Mohapatra et al did. The results of classification for Mohapatra features are

shown in table 4. By comparing table 2 through 4, it is seen that proposed features yield better performance than two

recent works. In this paper we introduced appropriate features based on those features that pathologists consider to

differentiate leukocytes. These appropriate features are: 6 color features from both nucleus and cytoplasm (the median of

hue and saturation of both nucleus and cytoplasm and standard derivation of hue and saturation of cytoplasm); 6

geometric features (the number of segments of nucleus, compactness, eccentricity and solidity) from only nucleus

(except nucleus to cytoplasm area ratio and area of the entire cell); 6 statistical features (average intensity, average

contrast, smoothness, skewness, uniformity and entropy) from only cytoplasm; 7 moment invariants features from only

cytoplasm of each WBC. It should be mentioned that in this paper, the features are wisely extracted from both nucleus

and cytoplasm, not only from nucleus or cytoplasm. As segmentation is a crucial step in automatic leukocytes detection

systems, accurate segmentation of both nucleus and cytoplasm is needed for using our proposed features. With this point

of view, our results show that proper features as pathologists consider could give better performance and it is seen from

table 2 that correct rate for all leukocytes are above 93% which is a high and acceptable rate.

WBCs No. of

cells

Correct

rate Sensitivity Specificity

Neutrophils 38 % 98.5 % 98.6 % 98.2

Eosinophils 42 % 99.9 % 99.9 % 99.9

Monocytes 36 % 93.7 % 94.6 % 90.9

Lymphocytes 33 % 98.8 % 99.9 % 94.8

Total 149 % 97.73 % 98.25 % 95.95

TABLE 2. Results of classification for the proposed features

WBCs No. of

cells

Correct

rate Sensitivity Specificity

Neutrophils 38 % 97.2 % 97.9 % 95.1

Eosinophils 42 % 97.8 % 85.4 % 94.1

Monocytes 36 % 87.3 % 86.6 % 89.4

Lymphocytes 33 % 93.8 % 96.2 % 85.3

Total 149 % 94.03 % 91.53 % 90.98

TABLE 3. Results of classification for Veluchamy features

WBCs No. of

cells

Correct

rate Sensitivity Specificity

Neutrophils 38 % 90.9 % 91.7 % 88.7

Eosinophils 42 % 82.5 % 79.2 % 90.9

Monocytes 36 % 86.2 % 85.9 % 87.1

Lymphocytes 33 % 93.9 % 95.3 % 89.2

Total 149 % 88.38 % 88.03 % 88.98

TABLE 4. Results of classification for Mohapatra features

REFERENCES

[1] A. C. Guyton and J. E. Hall, Textbook of medical physiology: Elsevier Saunders, 2006.

[2] C. Fatichah, M. L. Tangel, M. R. Widyanto, F. Dong, and K. Hirota, "Parameter optimization of local fuzzy

patterns based on fuzzy contrast measure for white blood cell texture feature extraction," JACIII, vol. 16, pp.

412-419, 2012.

[3] S. H. Rezatofighi, H. Soltanian-Zadeh, R. Sharifian, and R. A. Zoroofi, "A new approach to white blood cell

nucleus segmentation based on gram-schmidt orthogonalization," in Digital Image Processing, 2009

International Conference on, 2009, pp. 107-111.

[4] V. Hoffbrand, P. Moss, and J. Pettit, Essential haematology: Wiley, 2006.

[5] N. Ritter and J. Cooper, "Segmentation and border identification of cells in images of peripheral blood smear

slides," in Thirtieth Australasian Computer Science Conference (ACSC2007), Ballarat Australia, 2007, pp. 161-

169.

[6] M. Hamghalam and A. Ayatollahi, "Automatic counting of leukocytes in giemsa-stained images of peripheral

blood smear," in Digital Image Processing, 2009 International Conference on, 2009, pp. 13-16.

[7] C. Fatichah, M. L. Tangel, M. R. Widyanto, F. Dong, and K. Hirota, "3d structuring element based multiscale

morphology for bone marrow white blood cell segmentation," in The 2010 International Symposium on

Intelligent, Tokyo.

[8] O. Gmez, J. A. Gonzlez, and E. F. Morales, "Image segmentation using automatic seeded region growing and

instance-based learning," in Progress in pattern recognition, image analysis and applications, ed: Springer,

2007, pp. 192-201.

[9] J. Kan, L. Qing-Min, and D. Sheng-Yang, "A novel white blood cell segmentation scheme using scale-space

filtering and watershed clustering," in Machine Learning and Cybernetics, 2003 International Conference on,

2003, pp. 2820-2825 Vol.5.

[10] L. Yang, P. Meer, and D. J. Foran, "Unsupervised segmentation based on robust estimation and color active

contour models," Information Technology in Biomedicine, IEEE Transactions on, vol. 9, pp. 475-486, 2005.

[11] S. Eom, S. Kim, V. Shin, and B. Ahn, "Leukocyte segmentation in blood smear images using region-based

active contours," in Advanced Concepts for Intelligent Vision Systems, 2006, pp. 867-876.

[12] F. Zamani and R. Safabakhsh, "An unsupervised gvf snake approach for white blood cell segmentation based on

nucleus," in Signal Processing, 2006 8th International Conference on, 2006.

[13] S. Mohapatra, S. S. Samanta, D. Patra, and S. Satpathi, "Fuzzy based blood image segmentation for automated

leukemia detection," in Devices and Communications (ICDeCom), 2011 International Conference on, 2011, pp.

1-5.

[14] M. Subrajeet, P. Dipti, and K. Kundan, "Unsupervised leukocyte image segmentation using rough fuzzy

clustering," ISRN Artificial Intelligence, vol. 2012, 2012.

[15] N. Theera-Umpon, "White blood cell segmentation and classification in microscopic bone marrow images," in

Fuzzy systems and knowledge discovery, ed: Springer, 2005, pp. 787-796.

[16] P. Yampri, C. Pintavirooj, S. Daochai, and S. Teartulakarn, "White blood cell classification based on the

combination of eigen cell and parametric feature detection," in Industrial Electronics and Applications, 2006

1ST IEEE Conference on, 2006, pp. 1-4.

[17] P. Gmez-Gil, M. Ramrez-Corts, J. Gonzlez-Bernal, . Pedrero, C. I. Prieto-Castro, D. Valencia, et al., "A

feature extraction method based on morphological operators for automatic classification of leukocytes," in

Artificial Intelligence, 2008. MICAI'08. Seventh Mexican International Conference on, 2008, pp. 227-232.

[18] P. Hiremath, P. Bannigidad, and S. Geeta, "Automated identification and classification of white blood cells

(leukocytes) in digital microscopic images," IJCA special issue on recent trends in image processing and pattern recognition RTIPPR, pp. 59-63, 2010.

[19] S. Shivhare and R. Shrivastava, "Automatic bone marrow white blood cell classfication using morphological

granulometric feature of nucleus," 2012.

[20] M. Veluchamy, K. Perumal, and T. Ponuchamy, "Feature extraction and classification of blood cells using

artificial neural network," American Journal of Applied Sciences, vol. 9, p. 615, 2012.

[21] R. C. Gonzalez and R. E. Woods, Digital image processing: Pearson/Prentice Hall, 2008.

[22] R. S. Montero and E. Bribiesca, "State of the art of compactness and circularity measures," in International

Mathematical Forum, 2009, pp. 1305-1335.