Unsupervised Leukemia Cells Segmentation Based On Multi-Space Color Channels

Leukemia is a type of cancer that originates in the bone marrow and is characterized by abnormal proliferation of white blood cells. In order to have correct identification of lymphoblasts, hematologists examine blood blades of the patient. A low cost and efficient solution to facilitate the work of these experts is the use of systems to examine blood microscopic images. Segmentation is considered a crucial step to developing these systems. In this paper, we propose an automatic segmentation technique that uses two-color systems and the clustering algorithm K-means. The proposed approach is evaluated on three public image databases with different characteristics and performance measures used are: accuracy, specificity, sensitivity and Kappa index. The results obtained in the experiments have Kappa index of 0.9306 in ALL-IDB 2, 0.8603 in BloodSeg and 0.9119 in Leukocytes database. These measures outperform other methods of literature.