Application of Support Vector Machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation

• Main Authors: Mohammed, Emad A., Far, Behrouz H., Naugler, Christopher, Mohamed, Mostafa M. A.
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Bioinformatics; Chronic Lymphocytic Leukemia (CLL); Classification algorithms; Clustering algorithms; Color image segmentation; Image color analysis; Image segmentation; K-means; Machine learning; Support vector machines; SVM; Training; White Blood Cell (WBC)
• DOI: 10.1109/HealthCom.2013.6720751

Chronic lymphocytic leukemia (CLL) is the most common type of blood cancer in Canadian adults. The relative 5-year survival rates for CLL in Canada is decreasing. CLL cell morphology maybe similar to normal lymphocytes and require a hematopathologist examination for diagnosis. There are a low number of related works on image analysis in CLL. This paper focuses on lymphocyte color cell segmentation using Support Vector Machine (SVM) and k-means clustering algorithms. The algorithm overcomes the occlusion problem when lymphocytes are tightly bound to the surrounding Red Blood Cells. Over and under-segmentation problems are significantly reduced. In this paper we used 440 lymphocyte images (normal and CLL), in which 140 images are used for segmentation accuracy measurement and 12 images for SVM training. The algorithm obtained 98.43% maximum accuracy for nucleus segmentation, and 98.69% for cell segmentation. The cytoplasm region can be extracted by 99.85% maximum accuracy with simple mask subtraction.