DETECTION OF LEUKEMIA IN MICROSCOPIC IMAGES USING DIGITAL IMAGE PROCESSING TOOL IN MATLAB

This research describes leukemia detection techniques. Leukemia is a type of cancer affecting blood-forming tissues of the spleen, bone marrow, and lymph system.  Its symptoms are headache, fatigue, weakness, mouth sores, sternal tenderness, gingival hyperplasia, minimal hepatosplenomegaly, and lymphadenopathy. To avoid the rapid progression of immature hematopoietic cells, it is very important to detect leukemia at an early stage. Existing methods of diagnosis are –medical history and physical examination, complete blood count, bone marrow aspiration, cytogenetic analysis, and immunohistochemistry. These methods are time-consuming, not cost-effective, and totally dependent on medical personnel [1]. To get rid of these problems a digital image processing tool in MATLAB is used. So many methods of image processing are used for the identification of red blood cells and immature or mature white blood cells and different diseases like anemia, malaria, etc. can also be diagnosed by using digital image processing methods[2]. The main objective of this research work is to detect leukemia cells and count their area, perimeter, roundness, and standard deviation. For detection of immature blast cells, a number of methods are used like histogram equalization, linear contrast stretching, ostu thresholding, some morphological techniques like area opening, area closing, dilation, and erosion they also help to identify the acute or chronic stages of leukemia. Previously microscopic images were inspected by hematologists and it’s really very time-consuming, but the technique now is used is digital image processing by which neither patient nor doctor has to wait for his/her report.  Blood is taken from the patient’s arm and slides are prepared then microscopic slides images are captured and uploaded in digital image processing tool MATLAB software then it gives accurate results that whether the patient has acute or chronic leukemia or he/she does not have leukemia [3]. Microscopic images are processed using image processing techniques such as image enhancement, segmentation, feature extraction, and classification. The initially RGB image is read then RGB image to gray image conversion after the image is converted into binary image and segmentation is also done which will segregate white blood cells from all other blood components i.e. erythrocytes and platelets. The image conversion into the binary image area opening is done. Then hole filling and after that boundary is detected for each and every cell, for this purpose Sobel operator is used and it also differentiates between overlapped and non-overlapped cell, with the value of major axis, minor axis, convex hull, and standard deviation.[4] This whole process is performed using a digital image processing tool in MATLAB. ‘Region props’ properties are used to find area, roundness, centroid, the major and minor axis of cells. In this research shape-based features are used because it is very easy for the detection of white mature and immature cells and the shape of a cell. The early and fast detection of leukemia is very important because it helps aid in providing treatment better. The proposed result gives promising results for varying image quality and even so any images can be detected. This research will be helpful for those who cannot afford fees for detection of leukemia and for those also who have less time and due to this process treatment of leukemia can be done earlier.[5]