An Automatic Detection and Counting of Leukocytes Using Microscopic Images with Digital Image Processing

Complete Blood Count (CBC) is an important and primary blood test that is required by many physicians to get an overall view about patients with many diseases. The blood examination will indicate several diseases like cancer, HIV/AIDS, diabetes, anemia, and coronary heart disease which are popular diseases. The blood count offers a measure of the concentration of the Red Blood Cells (RBC), White Blood Cells (WBC), and platelets. Leukemia may be a cluster of medical specialty malady that sometimes affects blood, bone marrow, lymph nodes which characterized by overproduction of abnormal white cells which are unable to fight infection. Careful microscopic examination of stained blood smear or bone marrow aspirate is that the solely thanks to effective destination of leukemia. Recently several scientists have performed tremendous analysis in serving to the hematologists within the issue of segmenting the blood cells within the early of prognosis. This projected work aims to segment the blood cell images of patients suffering from acute leukemia using a Fuzzy C-Means (FCM) and Adaptive K-Means (AKM) clustering algorithm. The integrated clustering techniques have produced comprehensive output images with minimal filtering process to remove the background scene. And compare the performance of these two clustering algorithms.