Abstract—White blood cell diagnosis is usually performed by doctors manually through visual examination of blood smears under microscope. It is a time consuming, tedious, and susceptible to error, so an automatic process using computerized system is preferable. In this automatic process, segmentation and classification of white blood cell are the most important stages. An automatic segmentation technique for microscopic white blood cell images focusing on images from fresh blood smears is proposed in this paper. The segmentation is conducted using a proposed method that consists of an integration of several digital image processing algorithms. Sixty microscopic blood images were tested, and the proposed method obtained 92% accuracy for cytoplasm segmentation and 89% accuracy for nucleus segmentation. Keywords—white blood cell, blood smear,

segmentation, image processing.

I. INTRODUCTION HERE are three types of cells in normal human blood: red blood cells, white blood cells or

leukocyte, and blood platelets. Generally, red blood cells are simple and similar. While white cells contain nucleus and cytoplasm and there are different types of them. White cells are categorized into five groups: neutrophil, eosinophil, basophil, monocyte and lymphocyte. The texture, color, size and morphology of nucleus and cytoplasm make differences among these groups.

In the traditional process, doctors analyze human blood by microscope. This manual process is time consuming, tedious and susceptible to error, so an automatic system seems necessary and helpful. The automatic system may require four stages: acquisition, detection, feature extraction, and classification. In the second stage, cell segmentation is used to produce a number of single cell images. Then each single cell image is segmented into three regions: nucleus, cytoplasm, and background. In the third step, feature vectors of color, texture, and shape of the segmented cell and its nucleus are extracted. In the last step

according to the extracted features, each white blood cell is labeled by a classifier. The most important stage is the cell segmentation because the accuracy of segmentation plays a crucial role in the next stages.

Several researches have previously proposed methods for white blood cells segmentation. One method of white blood cell image segmentation is a method based on Fuzzy C-Means (FCM) clustering performed by Theera-Umpon [1]. The author stated that the method is good and yields better segmentation than the segmentation done by humans. Segmentation based on clustering with FCM was improved by Chinwaraphat, et al. [2]. The authors modified the standard FCM clustering making the extraction area of the nucleus and the cytoplasm more efficient than the standard FCM.

Another segmentation method is a method based on morphological operations proposed by Dorini, et al. [3] for the separation of the nucleus and cytoplasm of white blood cells from the blood image. The author explored the scale-space properties on the toggle operator to improve the segmentation accuracy. This method could be applied to the images in large quantities and produced good result for a variety of cell appearance and image quality.

Another research in this area is a framework developed by Sadeghian, et al. [4] that performed segmentation of white blood cells using an integration concept of digital image processing operations. This method consists of two stages: segmentation of nucleus based on morphological analysis and segmentation for separating cytoplasm based on color intensity.

Other methods based on color based segmentation were proposed by P.S. Hiremath, et al. [5] and F.D. Ratnasari [6]. There were also some researches using color segmentation based on other color spaces. J. Duan and L. Yu [7] proposed a white blood cell segmentation method based on HSI color space which the authors claimed that it makes better performance. K. Jiang et al. [8] proposed a white blood cell segmentation method that performs well in HSV space which is more appropriate than RGB space due to its low correlation.

White Blood Cell Segmentation for Fresh Blood Smear Images

Firdaus Ismail Sholeh Department of Computer Science and Electronics Universitas Gadjah Mada, Yogyakarta, Indonesia

Email: firdaus_is@mail.ugm.ac.id

T

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In this research, a segmentation method based on several digital image processing methods was developed. The method was designed to appropriate with obtained data which are blood images from fresh blood smears. Fresh blood smears are obtained immediately after blood smears have been made from human blood. Fresh blood smear is used because there is a noticeable difference in terms of color and morphology of the cells compared to preserved blood smears. Furthermore, in fact, the blood smear used in hospitals to diagnose patients is the fresh blood smear. A sample of fresh blood smear image is shown in Figure 1. Sample images for each white blood cell type are shown in Table 1.

II. MATERIALS AND METHODS The integration of several digital image processing

algorithms is used for the process of image segmentation of the white blood cells from microscopic blood images. The image segmentation

method is performed on the whole white blood cells followed by nucleus segmentation. The flowchart of white blood cell image segmentation is shown in Figure 3. The segmentation method was developed using Aforge Framework.

The main stages of the white blood cell image

segmentation are described as follows. 1) Noise Reduction

Segmentation process begins by performing median filtering to reduce noise arising at image acquisition. 2) Elimination of Red Blood Cell and Cytoplasm Area

There are three main areas in blood image that are white blood cells, white blood cells, and cytoplasm. Area of red blood cells that has dominant red color intensity and area of cytoplasm that has dominant green color intensity can be extracted easily. Extraction of the area of red blood cells and the cytoplasm is used to get the candidate area of white blood cells. 3) Elimination of Platelet Cell and Noise Area

Result of above process is a binary image that may have noises that are some areas that are not areas of white blood cells. It is also possible that there are areas of platelet cells have a color similar to the color of white blood cells, but can be distinguished from its

TABLE I SAMPLE IMAGES FOR EACH WHITE BLOOD CELL TYPE

Cell type A cropped sample cell image

Neutrophil

Lymphocyte

Monocyte

Eosinophil

Basophil

Fig. 1. A sample of fresh blood smear image

Start

Blood image

Median filteringR channel extraction,

Otsu ThresholdingG channel extraction,

Otsu Thresholding

Addition

Opening,Area filteringAddition

White blood cell image

Blood image

Red blood cell overlay

Cytoplasm overlay

White blood cell overlay

End

White blood cell overlayRegion growing

Closing,Opening,

Area filtering

Fig. 3. Flowchart of white blood cell image segmentation

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size. To clean these areas, the morphological opening operation followed by noise filtration is used. 4) Region Growing

For some images, the binary image of the results of the above process is still not fully covers overall areas because white blood cells area may contain cytoplasm pixel color that is similar to the color of red blood cells which is lost in the previous process. The region growing process is done to expand the existing areas in order to cover overall areas of the white blood cells. 5) Cell Shape Smoothing

A morphological closing and opening are performed to smooth the cell shape followed by area filtration for noise removal. Finally the result image is summed with the blood image to get the white blood cell image.

Once the image of the white blood cells is obtained, the image segmentation for the nucleus area of the white blood cell is performed. The flowchart of the nucleus image segmentation is shown in Figure 4.

White

blood cell image

Blue chanel extraction,Otsu Thresholding,

Hole filling,Size filtering

Addition Nucleus imageStart

EndNucleus image overlay

Fig. 4. Flowchart of nucleus image segmentation

The area of the nucleus has a greater intensity of

blue color compared with the cytoplasm, so segmentation can be done by extracting the blue color channel and thresholding. The process is proceed with noise reduction by hole filling and followed by area filtering. Furthermore, with the addition operation to the image of the white blood cells, the nucleus image is obtained.

The segmentation testing is done by applying to 76 blood images. The determination of whether a segmented image is nicely done with the object-based observations of the area segmentation results whether already sufficiently covered all regions of white blood cells or not.

III. RESULTS AND DISCUSSION The experiments were performed to 60 blood

images with size of 600x450 pixels producing 76 images of white blood cells. The average time of segmentation is 1.228 seconds. Some sample images of white blood cells and their segmentation results are shown in Table 2. The results of cell segmentation area are marked with white lines and the results of the segmentation nucleus area are marked with yellow lines.

Generally, with the proposed method, the presence of white blood cells contained in the blood images can be identified. The problem that arises is how accurate

the searching of entire pixel included in the cell area. Based on observations of 76 cell images, there are 6 images containing segmented cell that not covers overall cell pixel and there are 8 images that not cover well overall nucleus pixel. Thus, based on object based accuracy measurement, it can be measured that the accuracy of the segmentation of white blood cells is 92.05% and the accuracy of the segmentation nucleus is 89.47%. The results of image segmentation for each type of white blood cell are shown at Table 3.

The searching process of white blood cell pixels intensely depends on the region growing contained in the segmentation method. Determination of intensity range that is not appropriate in the region growing process cause under growing and over growing. Under growing is a condition in which the region growing covers only part of the cell area. Under growing may occur due to the range of parameters for region growing intensity is too low. While over growing is a condition in which the region growing process over the entire cell area. Over growing may occur due to the range of parameters for region growing intensity is too high. The result of image segmentation of white

TABLE 2 SOME WHITE BLOOD CELL SEGMENTATION RESULTS

Cropped cell image Segmentation result

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blood cells that are bad because under growing and over growing is shown in Table 4.

The bad segmentations because under growing, as happened to the 1st, 2nd, 3rd, 4th, and 6th image in Table 4, may be solved by expanding the intensity range of region growing, but it causes overgrowing in many cases where white blood cells intersect with red blood cells because there are a lot of similarities of the color intensity of some parts of the both cell groups.

The bad segmentation due to over growing as occurs in 5th image may be resolved with a morphology operation with a structural element that form a large circle, but the operation can cause the time required for the segmentation becomes longer.

The bad segmentations also occur at the process of nuclei segmentation of white blood cell images. The error occurs due to the determination of the threshold

value which is not appropriate on the global thresholding. Errors are also occurred because the determination of the cell area itself is not appropriate. The results which have bad segmentation of the nucleus are shown in Table 5.

IV. CONCLUSION This paper has demonstrated a proposed method for

segmenting white blood cells from fresh blood smear images using integration of concepts in digital image processing. Sixty microscopic blood images were tested. Generally, with the proposed method, the presence of white blood cells contained in the blood images can be identified. Using object based accuracy measurement, the proposed method obtained 92% accuracy for cytoplasm segmentation and 89% accuracy for nucleus segmentation. This work is to be carried out in the next phase of study.

TABLE 4 RESULTS OF BAD WHITE BLOOD CELL SEGMENTATIONS

Cropped cell image Segmentation result

TABLE 5 RESULTS OF BAD NUCLEUS SEGMENTATION

Cropped cell image Segmentation result

TABLE 3 SEGMENTATION RESULT SUMMARY

Cell type Segmented cells

Good segmented cells

Bad segmented cells

Good segmented nuclei

Bad segmented nuclei

Neutrophil 37 33 4 31 6

Lymphocyte 29 28 1 29 0 Monocyte 5 5 0 4 1

Eosinophil 4 4 0 4 0

Basophil 1 0 1 0 1

Total 76 70 6 68 8

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ACKNOWLEDGMENT The author is gratefully acknowledged and thanks

Laboratory of Clinical Pathology, Faculty of Medicine, Universitas Gadjah Mada for service for data acquisition.

REFERENCES [1] N. Theera-Umpon, “Patch-Based White Blood Cell Nucleus

Segmentation using Fuzzy Clustering”, ECTI Transactions on Electrical Engineering, Electronics, and Communications, Vol. 3, No. 1, pp. 15-19, 2005.

[2] S. Chinwaraphat, A. Sanpanich, C. Pintavirooj, M. Sangworasil, and P. Tosranon, “A Modified Fuzzy Clustering for White Blood Cell Segmentation”, The 3rd International Symposium on Biomedical Engineering, 2008.

[3] F. Sadeghian, Z. Seman, A.R. Ramli, B.H.A. Kahar, and M.I. Saripan,” A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing”, Biological Procedures Online, Vol. 11, No. 1, 2009.

[4] P.S. 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”, 2010.

[5] F.D. Ratnasari, “Identifikasi dan Klasifikasi Jenis Sel Darah Putih Dengan Pengolahan Citra Digital”, Thesis, Departement of Physics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Yogyakarta, 2010.

[6] J. Duan and L. Yu, “A WBC Segmentation Method Based On HSI Color Space”, Proceedings of IEEE IC-BNMT 2011.

[7] L.B. Dorini, R. Minetto, and N.J. Leite, “Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis”, IEEE Journal of Biomedical and Health Informatics, Vol. 17, No. 1, 2013.

[8] K. Jiang, Q.M. Liao, S.Y. Dai, “A Novel White Blood Cell Segmentation Scheme Using Scale-Space Filtering and Watershed Clustering”, Proceedings of the Second International Conference on Machine Learning and Cybernetics, pp. 2820-2825, 2003.

[9] R.C. Gonzalez, and R.E. Woods, Digital Image Processing, Prentice Hall, New Jersey (2008).

[10] A. Kirillov, AForge.NET Framework, http://www.aforgenet. com/framework/, [accessed December 10, 2012].

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