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2011 International Conference on Image Information Processing (ICIIP 2011)

Proceedings of the 2011 International Conference on Image Information Processing (ICIIP 2011) 978-1-61284-861-7/11/$26.00 ©2011 IEEE

Blood Microscopic Image Segmentation using Rough Sets

Subrajeet Mohapatra, Dipti Patra Electrical Engineering,

National Institute of Technology Rourkela Rourkela, Odisha, India [email protected]

Kundan Kumar Electronics and Electrical Communication Engineering,

Indian Institute of Technology Kharagpur Kharagpur, India

[email protected]

Abstract— Hematological disorders are mostly identified based on characterization of blood parameters i.e. erythrocytes, leukocytes and platelets. Microscopic examination of leukocytes in blood slides is the most frequent laboratory investigation performed for malignancy detection. Hematological examination of blood is an indispensable technique still today and solely depends on human visual interpretation. Such examination are subjected to inter and intra-observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia can help in the planning of suitable surgery and chemotherapy, and generally improve the quality of patient care. Microscopy cell image analysis is a tool which facilitates conventional blood examination for disease detection using quantitative microscopy. Thus microscopic image analysis serves as an impressive diagnostic tool for hematological disease (leukemia, malaria, psoriasis, AIDS etc) recognition. The present paper aims at leukocyte or white blood cell (WBC) segmentation which can assist in acute leukemia detection. A rough set based clustering approach is followed for color based segmentation of WBC. The segmented nucleus and cytoplasm can be used for feature extraction which can lead to classification of a leukocyte into mature lymphocyte or lymphoblast.

Keywords-Leukocyte; image segmentation; rough sets; clustering; quantitative microscopy

I. INTRODUCTION Cellular components of the blood are considered important, as the blood cells are easily accessible indicators of disturbances in their organs of origin or degradation which are much less accessible for diagnosis. Thus, changes in the erythrocyte, leukocytes, and platelets allow important inference to be drawn about various hematological disease conditions [1]. Among all, abnormalities of erythrocytes, leukocytes and platelets are considered crucial and needs immediate medication. The present work basically deals with diseases of white blood cells (WBC) or leukocytes. Alterations in WBC can be neoplastic or non-neoplastic. One of the potentially fatal neoplastic disorder i.e. leukemia is considered as our subject of study. Leukemia can be defined as neoplastic proliferations of hemopoietic cells. Specific genetic changes are responsible for malignant transformation of cells and their progeny forming a clone of leukemia cells. Leukemia can be

understood as a hematological malignancy with increased numbers of myeloid or lymphoid blasts. Leukemia can be acute or chronic depending on the severity of the disease. Practical classification of leukemia is quite complicated and can be categorized on the basis of morphologic findings, genetic abnormalities, putative etiology, cell of origin, immunophenotypic qualities, and clinical characteristics. French, American, British (FAB) classification and World Health Organization (WHO) classification are two widely used protocols for leukemia categorization [2]. Both fundamentally divide leukemia's into myeloid and lymphoid types, depending on the origin of the blast cell. In the present work we consider acute lymphoblastic leukemia (ALL) as our research focus. Regardless of advanced techniques like flow cytometer, immunophenotyping, molecular probing etc, microscopic examination of blood slides still remains as most economical way for initial screening of leukemia patients. Manual examination of blood slides are subjected to inconsistent and subjective reports. Thus there is always a need for a cost effective and robust automated system for leukemia screening which can improve the diagnostic accuracy without bias. Image segmentation is an essential component in all image analysis systems. Required extent of accuracy is very high in automated leukemia detection and solely depends on leukocyte segmentation. Since there is no standard segmentation method for all images, thus specific algorithm has to be developed for leukocyte images. Segmentation methods can be broadly classified as supervised or unsupervised. Over years many blood smear image segmentation methods have been proposed. One of the major issues in such segmentation is touching and overlapping cells due to accumulation of high amount of leukocytes or red blood cells. Variable staining and uneven lightening condition makes the segmentation more difficult [3][4][5][6][7]. Blood smear or bone marrow segmentation methods proposed in the literature are mostly threshold based, region based or edge based schemes. Color based segmentation is also a better procedure followed for segmentation of blood images. A two step segmentation process using HSV color model is used in


Proceedings of the 2011 International Conference on Image Information Processing (ICIIP 2011)

[8]. Color segmentation procedure applied to leukocyte images using mean-shift is described in [9]. The use of shape analysis into WBC segmentation was introduced in [10]. Cell segmentation using active contour models is presented in [11]. There are several similar findings on blood cell segmentation in the literature. Due to complex nature of the blood smear images and variation in slide preparation techniques much work has to be done to meet real clinical demands. In this work, a rough based clustering approach is employed for robust blood smear image segmentation. Rough sets theory [12] was introduced by Pawlak which serves as a tool for dealing with uncertain, incomplete and vague data. Rough set can be defined as a formal approximation of a crisp set in terms of a pair of sets which give the lower and the upper approximation of the original set [13]. It has been used in many advanced applications like web data mining, predictive maintenance, medicine etc [14]. K-means algorithm is a simple clustering technique which is independently employed in various problems including image segmentation. An important drawback of this simple clustering technique is it gets trapped in local minima. A hybrid combination of rough sets and K-means is employed here for image data clustering under color segmentation framework. Rest of the paper is organized as follows: Section II describes the schema of the proposed method. Experimental results are presented in Section V and Section VI presents a detailed analysis on the results obtained. Finally, Section VII provides the concluding remarks.

II. MATERIAL AND METHODS

A. Blood Smear Preparation

Blood samples were collected at Ispat General Hospital, Rourkela, India through randomization. Subsequently blood smear is prepared and stained using Leishman for visualization of cell components. The images were captured with a digital microscope (Carl Zeiss India) under 100X oil immersed setting and with an effective magnification of 1000. Few images with permission from University of Virginia were also considered for experimental purposes. Figure 1 presents a set of sample stained leukocyte images. The data set is a mixture of lymphocytes and lymphoblasts. There are 100 images collected from Ispat General Hospital, Rourkela, India and 8 images are collected from University of Virginia. Manual segmentation was performed by Dr. Sanghamitra Satpathy, Hematologist, Department of Pathology, Ispat General Hospital, Rourkela, India. Each hand segmented image consists of nucleus, cytoplasm and back ground.

Figure 1. Sample Stained Leukocytes

B. Sub Imaging The input peripheral blood smear images are relatively larger with more than one leukocyte per image. As per the requirement, region of interest (ROI) must contain a single leukocyte only and is obtained by automatic cropping of the original input image. This is desired as every leukocyte in the input image has to be evaluated for classifying it as a blast cell. Thus sub images containing single nucleus per image are obtained using bounding box [15] technique. We use simple K-means color based clustering to obtain all the blue WBC nucleus of the entire image. Using image morphology we obtain the centroid of each nucleus and a square image is cropped around each nucleus such that entire cell will be within the cropped sub image as shown in Figure 2. Again remapping with the original image we can restore the color components and color sub images are obtained as shown in Figure 3. Sub images containing single lymphocytes only were obtained and can now be used for further processing.

Figure 2. Initial K-means Segmentation

Figure 3. Cropped Sub Images

C. Preprocessing Noise may be accumulated during image acquisition and due to excessive staining. All the test images are subjected to selective median filtering followed by unsharp masking [16]. Incorporation of adaptive threshold into the noise detection process led to more reliable and more efficient detection of noise. Minute edge details of the microscopic images are perfectly preserved even after median filtering. Unsharp masking is performed to sharpen the image details making the segmentation process easier.

D. Color Conversion Typically images generated by digital microscopes are usually in RGB (Red, Green and Blue) color space. A number of other color spaces or color models have been suggested in literature for various specific purposes. In the present paper we use



L*a*b* color model for reduced color feature based clustering. The L*a*b* version of two sample images are shown in Figure 4. The L*a*b* color space is a color representation technique which consists of a luminosity layer L*, chromaticity layer a* and chromaticity layer b*. The color components i.e. a* and b* are used as features in the clustering process. Computation time is an important issue in all feature based clustering problems with large data sets. Use of two color features (a* and b*) instead of three (red, green and blue) reduces the computational time drastically.

Figure 4. Sub Images in L*a*b* Color Space

E. Image Segmentation Recognition of leukemia in blood samples is based on morphological variation of WBC. Such alterations can only be measured with segmented nuclei and cytoplasm. The present paper focuses on nucleus and cytoplasm extraction from the background using a hybrid approach i.e. rough K-means clustering. The segmented regions can be used for feature extraction for accurate leukemia detection. In the following sections we present cluster based image segmentation which includes K-means and rough K-means clustering.

III. CLUSTER BASED IMAGE SEGMENTATION Clustering is an unsupervised classification of data patterns into homogeneous groups or clusters. It is a difficult problem which has been addressed by various researchers in diversified areas such as pattern recognition, data mining, image processing, biology, psychology, and marketing etc. Popular clustering algorithms used for image segmentation include K-means [17], Fuzzy C Means [18] and Fuzzy Possibilstic C means[19].

A. K-means Clustering K-means is a center-based clustering algorithm which is efficiently employed for clustering large databases and high-dimensional databases. The objective of a center-based algorithm is to minimize its objective function and is well suited for convex shape clusters and fails drastically for clusters of arbitrary shapes [20]. K-means algorithm as proposed by Macqueen (1967) can be summarized as follows:

1. Choose initial centroid mi for each cluster. 2. Assign each data pattern (point) Xk to the cluster Ui

for the closest mean. 3. New updated centroid was obtained using the relation

as defined as,

i

UX ki C

Xm ik

∑ ∈=

where iC is the number of objects in cluster iU . 4. Repeat step 2 and 3 until the updated centroid

becomes stable

Along with local optimum problem there exist additional challenges in data clustering i.e. the clusters tend to have vague and imprecise boundaries. Such overlapping conditions can be handled effectively by fuzzy or rough based techniques. Rough sets are computationally efficient in comparison to fuzzy methods. Further rough K-means clustering approach for a given data set results with reasonable set of lower and upper bound. Thus rough K-means was employed for leukocyte segmentation. Rough sets and rough K-means algorithm are presented in the following sections.

B. Rough Sets The principle of rough set is based on representation of rough or imprecise information in terms of exact concepts i.e. lower and upper approximation. Lower and upper approximations are obtained using an indiscernible relation based on the attributes of the objects in a particular domain. The set of objects which definitely belong to the vague concept are classified under lower approximation, where as objects which possibly belong to the same are categorized as upper [21]. The difference of upper and lower approximation will result with objects in the rough boundaries.

C. Rough K-means Clustering Leukocyte segmentation is a challenging problem in pathological image processing. Color difference between background stain and cytoplasm is minimal, thus it is difficult to extract cytoplasm from the background. Such imprecise condition motivated us to employ rough based clustering for leukocyte segmentation.

A rough set Y is characterized by its lower and upper approximations YB and YB respectively. In rough context an object Xk can be a member of at most one lower approximation. If Xk ∈ YB of cluster Y, then concurrently Xk

∈ YB of the same cluster. Whereas it will never belong to other clusters. If Xk is not a member of any lower approximation, then it will belong to two or more upper approximations. Updated centroid mi of cluster Ui is computed as

⎩⎨⎧ ≠−

=otherwiseL

UBUBifLm ii

i2

1 φ (2)

where,

( )

ii

UBUBX kup

i

UBX klow UBUB

Xw

UB

XwL iikik

−+=

∑∑ −∈∈1



( )

ii

UBUBX kup

i

UBX klow UBUB

Xw

UB

XwL iikik

−+=

∑∑ −∈∈2

The parameters wlow and wup correspond to relative weighting factor for lower and upper approximation respectively towards centroid updation. In this process the weight factor for lower approximation ( )iUB is higher than that of rough boundary

( )ii UBUB − , i.e. ( )uplow ww > . Where iUB signifies the number of members in the lower approximation of cluster Ui,

where as ii UBUB − is the number of members present in

the rough boundary within the two approximations. The detailed rough K-means algorithm is presented below.

D. Algorithm i. Assign initial centroids mi for the K clusters.

ii. Each data object Xk is assigned either to the lower approximation YB or upper approximation YB of cluster Ui, by computing the difference in its distance

( ) ( )jkik mXdmXd ,, − from cluster centroid pairs mi and mj.

iii. If ( ) ( )jkik mXdmXd ,, − is less than a particular

threshold T, then ik UBX ∈ and jk UBX ∈ and Xk cannot be a member of any lower approximation, else

ik UBX ∈ such that euclidean distance ( )ik mXd , is minimum over the K clusters.

iv. Compute new updated centroid for each cluster Ui using equation 2.

v. Iterate until convergence, i.e., there are no more data members in the rough boundary.

Rough K-means algorithm is completely governed by three

parameters such as wlow, wupper and T. The parameter threshold can be defined as relative distance of a data member Xk from a pair of cluster centroids mi and mj. These parameters have to be suitably tuned for proper segmentation. So in the proposed scheme a performance metric is considered for obtaining the optimum parameters.

IV. PROPOSED LEUKOCYTE SEGMENTATION As peripheral blood smear images are relatively larger and

contain more than one leukocyte. Sub images containing single leukocyte per image are desired and obtained as proposed in our previous work [22]. Blood images generated from digital microscope are usually represented using RGB color model and contains three color bands, i.e red, green, blue. a* and b* component of the leukocyte image are considered as two feature inputs for image segmentation using Rough K-means clustering. The proposed segmentation algorithm is applied on each sub image to separate the nucleus and cytoplasm from the background. The detailed segmentation approach in form of an algorithm is as follows:

i. Apply L*a*b* color space conversion on Irgb to obtain L*a*b* image i.e. Ilab.

ii. Construct the input feature vector using a* and b* components of Ilab.

iii. Each data pattern of the feature vector is assigned to a appropriate class using Rough K-means algorithm.

iv. Obtain the labeled image from the classified feature vector.

v. Reconstruct the segmented RGB color image for each class.

After segmentation each pixel of the leukocyte image is classified as one of the four clusters based on corresponding a* and b* values in L*a*b* color space. Clustered output in terms of a scatter plot for the image (Figure 5) is shown in Figure . Leukocyte images can be visually segmented into four regions i.e. nucleus, cytoplasm, RBC and background stain as suggested by the hematologist. Thus the number of classes K was considered as four. Rough sets are suitably modeled to represent the clusters as upper and lower approximation [21]. For acceptable clustering performance the parameters involved are required to be fine tuned. To optimally tune the parameters fitness metric P is considered as defined as,

∑=

=k

iiMP

1

Where,

∑∈

−=k

UXiki

ik

mXM

Figure 5. Feature Space Clustering results for Rough K- Means

V. EXPERIMENTAL RESULTS Improved Rough K-means clustering is employed for blood microscopic image segmentation. The proposed technique is applied on peripheral blood smear images obtained from two places as mentioned earlier. The superiority of the scheme is demonstrated by conducting two experiments.



A. Experiment 1 A leukocyte image of size 128×128 (Figure 5) is converted to L*a*b* color space. a* and b* component for every pixel is recorded and feature data set X of size 16384×2 is prepared. Each row of X represents a data pattern and redundancy among them was discarded. This concise form of X with size N×2 serves as an input towards pixel labeling problem through Rough K-means. Four data patterns were randomly selected from X as initial cluster centers. Euclidean distance d(Xk, m) between data pattern Xk and each cluster center mi was measured. Where k=1….N and i=1…K. Difference among the above distances for each pattern is computed. Depending on the value of this difference and a threshold each pattern was assigned to lower or upper approximation for corresponding clusters as described in Section III-C. This process was repeated till the upper approximation becomes empty. Accordingly each data pattern is assigned to one of the clusters. Four segmented images were obtained representing each cluster after remapping from the labeled image. Segmented results obtained from different clustering schemes are presented in Figure 6 for the leukocyte image sample (Figure 5). Each column represents a particular cluster and each row of the image indicates a particular clustering scheme. As we have four clusters so the image indicates four cluster outputs for each clustering scheme.

Figure 6. Original Image

Figure 7. Clustering results formed by different clustering techniques. K-means (a-d), FCM (e-h), PCM (i-l), RKM (Proposed)(m-p)

B. Experiment 2

The objective of this experiment was to tune the parameters wlow, wupper and T involved in image data clustering. In our experiment the role of wlow, wupper was studied by varying the values within the range 15.0 << loww and lowup ww −= 1 . No significant difference in clustering performance was observed. Keeping the values of 7.0=loww and 3.0=upw constant, T was varied between 0 to 5 in a step of 0.1. The number of elements present in the rough boundary was recorded for each value T. Figure 4 depicts the relationship between T and the number of elements in the rough boundary. It was observed that the rough boundary becomes empty for value of T equal to 1.4 or less for the given data set. Thus the optimum value of T will definitely lie between 0 to 1.4. The clustering fitness metric index M was employed to obtain the optimal threshold for image segmentation. The behavior of T with respect to the clustering metric is shown in Figure 5. The minimization of M reveals improvement in clustering

performance with maximization of T, i.e. T=1.4 for the given leukemia image.

VI. ANALYSIS Automatic leukemia detection from leukocyte images is only possible by morphological analysis of nucleus and cytoplasm region individually. Accuracy of detection solely depends on nucleus and cytoplasm region extraction from the leukocyte image. Thus use of proper segmentation technique is very essential for any medical image analysis system. Rough set based clustering was employed for nucleus and cytoplasm extraction. Choice of values of the parameter involved is to be estimated for improving the rough based segmentation performance. Accordingly the optimal threshold was obtained by optimizing a clustering performance metric. The performance of the proposed segmentation technique was validated for a good number of images and found to be superior to other standard color based clustering schemes.

VII. CONCLUSION AND FUTURE WORK Use of rough sets for leukocyte image segmentation for leukemia detection is the main theme of the paper. Encouraging segmentation results were obtained in contrast to standard schemes. In the present work a clustering performance metric was used to obtain suitable threshold



values for rough based clustering. Results obtained stimulate future works which includes stain and illumination independent blood smear image segmentation.

Figure 8. Characteristics of number of elements in the rough boundary with

respect to threshold

Figure 9. Characteristics of clustering performance with respect to Threshold

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