Image Segmentation, Image Classification, Leukocytes, using Tissue Images.

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1 Volume 119 No , ISSN: (on-line version) url: Image Segmentation and Pattern Classification of Leukocytes using Tissue Images. 1 M.Bhakiyalakshmi, 1 S.Prathiga, 1 B.Rachana, 1 P.G.kuppusamy 1 Department of Biomedical Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Avadi, Chennai kuppusamy@veltechmultitech.org ABSTRACT Blood cell segmentation and identification is the vital role for Haematologist to diagnose most blood diseases. The Immunological disorders like Leukaemia is due to abnormal count of White Blood Cells (WBC) in blood cells. The WBC classification and segmentation by manual analysis from blood smear is a time consumption and so proposed a method for noninvasive technique of accurate classification of blood cells from tissue images by using Digital Microscopic Endoscope Zoom Camera. These classification are proposed by using Neuro-Fuzzy Algorithm and counting with the help of Regression Equation. Keywords: Image Segmentation, Image Classification, Leukocytes, Regression Neuro-Fuzzy Algorithm and I.INTRODUCTION: Human blood cell consists of three types of cells Red Blood Cells (RBC), White Blood Cells (WBC) and Platelets. The presence of significant leukocytes determines the pathos condition in the blood which shows WBC plays a vital factor in blood count. Normally white blood corpuscles count ranges from 4,000-11,000 per microliter. WBC are the cells of immune system which may increase or decrease from their normal count due to some reasons such as Bacterial infection, Viral infection, Immune system disorder, inflammatory disease, side effects of chemotheraphy,etc. It consists of five subclasse such as Neutrophil, Basophil, Eosinophil, Lymphocytes and Monocytes.WBC must have 60-70% of Neutrophil, 2-4% of Eosinophil, 0.5% of Basophil, 30-40% of 515

2 Lymphocytes, 5-5.3% of Monocytes.The each subclasses also play important role in diagnosis of diseases.the abnormality in Neutrophil indicates Cyclic neutropenia, Reticular Dysgenesis, Leukocyte Adhesion Defects(LAD),etc.The abnormal increase of Eosinophil is due to Parasitic infection and most causes include allergies. The abnormal in the number of Basophil can occur as a response to Acute hypersensitivity reaction, Hypothyroidism, myeloproliferative disorders. The abnormal activity of Monocytes cause is due to some disorders such as Sepsis, CLL leukemia.it helps to diagnose different kinds of diseases.so we proposed the computer aided automatic and fast method II. METHODOLOGY: Leukocytes Segmentation and classification is an advanced technology and software-oriented which in processed in Matlab2014a.The implementation of this approach is carried out with the assistance of Digital Microscopic Zoom Camera by Non-invasive manner.the Acquired bloody tissue images are processed in Matlab2014a.The technique assisting under this software are Preprocessing and Algorithm implementation. of classification of images & counting of WBC cells. Then their imaging and classification process are adopted by Neuro fuzzy algorithm. In this method the input images are taken from Digital microscopic endoscope zoom camera and pre-process in MATLAB. The intensity of each pixel is used to vary the segmentation and classification of images from preprocessed image. The value of WBC count are calculated with the help of Regression Equation from the output Digital image. Likewise, these results may be applied on the medical and clinical sector, especially for haematologist to diagnose and detect disorder [1] Preprocessing In this face,the tissue images are preprocessed with the use of techniques like Gray scale conversion of image,use of Gaussian High pass filter followed by implication of Discrete fourier transform. [2] Algorithm implementation The proposed system follows Neuro-Fuzzy algorithm.this algorithm consist of Neural patterns of network which is 516

3 segmented on the basis of pixel rate.it follows Fuzzy logic which involves approximation of Nonlinear images into segmented linear one with the use of truth values of 0 and 1. types of Leukocytes such as Monocytes, Lymphocytes, Neutrophils, Basophils and finally Eosinophils. At the finishing point,the leukocytes and their subclassifications have been counted with the aid of Regression Equation. After the end of the following methods,the tissue images have been segmented as five Block Diagram Digital Microscopic Zoom Camera Tissue image MATLAB Preprocessing Final count of segemented leukocytes Regression equation Neural Networks + Fuzzy logic Neuro fuzzy algorithm PHASE-1 Tissue images are captured by using Digital microscopic zoom camera by noninvasive method.these tissues images are sanvaged in between blood and tissue surface.in our experimentation, image can be acquired at any input size and convert them into 150*150 pixel rate of image for 517

4 further processing which is illustrated in Fig a PHASE-4 The WBC count and subclass count are determined by using Regression equation.a regression equation models the dependent relationship of two or more variables. Preprocessed Output Fig a. Input image PHASE-2 Microscopic tissue images are proceeded for further pre-processing task like Gray scale conversion[fig b] Filtered imaged[fig c] Discrete Fourier Transform[fig d] Brighten image[fig e] Original Image (RGB) PHASE-3 The segmentation algorithm aims to separate every blood cell which depend upon their pixels for separation by using Neurofuzzy algorithm. Neurofuzzy algorithm results in a hybrid intelligent system that synergizes the technique by combining the human-like reasoning style of fuzzy systems with the learning connectionlist structure of neural networks. Fig b. Gray scale image 518

5 The luminance of the pixel value of gray scale images ranges from 0 to 255.Hence conversion of Gray scale image is of from 24 bits to 8 bit Sharpening image represents increased contrast between the brighter and the darker regions. Steps involved in leukocytes segmentation i) Input image from neuro fuzzy algorithm represented in fig. h. Fig d. Discrete Fourier Transform The output obtained represents an equivalent transform of the image which represents as the Fourier transform or frequency domain equivalent. In this domain each value intercepts a significant frequency in the spatial domain image. ii) Formation of binary image which has two possible values of 0 and 1.This indicates the colour of black and white for each pixel in the image in fig. i. iii) This step follows imfilling process which consist of holes it represents intensity values that helps to evaluate WBC count. This output image is represented in fig.j. iv) Finally WBC have been segmented into five types based on their pixel rate and intensity values. This has been represented in fig. k. Fig e. Sharpened Image 519

6 Segmented Leukocytes-5 Types eosinophil neutrophil Eosinophil Neutrophil lymphocyte basophil Lymphocyte Basophil 520

7 moncyte Monocyte RESULTS AND INFERENCE To illustrate this idea, 20 microscopic samples have been collected. The WBC images are segmented and classified with high accuracy. The WBC and subclasses of blood count are calculated using regression equation. In this paper, we proposed the method of segmentation and classification of WBC images with high accuracy. The neuro fuzzy algorithm has been used to classify 5 types of WBC images and their count. It is clear that the proposed method provides high accuracy compare to previous research. As a further work, we anlayse that the WBC and their subclass count will be useful for haematologist to diagnose different blood disease and immunity disorder. CONCLUSION The processing of microscopic blood tissue images also helps us to detect RBCs/platelets. Leukocytes has a different categories namely monocyte, lymphocytes, basophile, eosinophil, and neutrophil. To diagnose leukocyte for its various pathological conditions more subsets of classification is to be considered as the best option, which can be used to efficiently classify each category. To accomplish this task, we first need to detect WBCs in microscopic blood tissue images. Microscopic images and neuro fuzzy framework was involved to generate a effective result. REFERENCES 1. Prinyakupt and C. Pluempitiwiriyawej, Segmentation of white blood cells and comparison of cell morphology by linear and naíve Bayes classifiers, Biomed. Eng. OnLine, vol. 14, no. 1, p. 63, C. Reta, Segmentation and classification of bone marrow cells images using contextual information for medical diagnosis of acute leukemias, 521

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