Automated segmentation of Acute Lymphocytic Leukemia (ALL) subtypes by the combination of color space conversion and K-means cluster

Abstract

Leukemia is blood cancer, and it is one of the most common and deadly causes of death in the world. Morphologically, Leukemia cells are classified into three types of L1, L2, and L3 by the French‑American‑British (FAB) classification. A new method of automatically segmenting blast cells from microscopic blood smear images proposed in this research. This study proposes significant pre-processing to obtain high segmentation performance and presents a new combination of image processing approaches. specially, the five color spaces selected with K-means cluster to segment subtypes of Acute Lymphocytic Leukemia. The majority of the components of the performance color space were chosen based on their similarity with ground truth image through using five evaluation parameters. The proposed codes for Acute Lymphocytic Leukemia subtype accurate segmentation applied on local and public datasets (427 images). The best color space type was YIQ which had 87% performance for the public dataset with segmentation evaluation 96.24% for dice parameter

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