Improved Lymphocyte Image Segmentation Using Near Sets for ALL Detection

Light microscopic examination of peripheral blood smear is considered vital for diagnosis of various hematological disorders. The objective of this paper is to develop a fast, robust and simple framework for blood microscopic image segmentation which can assist in automated detection of hematological diseases i.e. acute lymphoblastic leukemia (ALL). A near set based clustering approach is followed for color based segmentation of lymphocyte blood image. Here, a novel distance measure using near sets has been introduced. This improved nearness distance measure has been used in a clustering framework for achieving accurate lymphocyte image segmentation. The nearness measure determines the degree to which two pixels resemble each other based on a defined probe function. It is essential as image segmentation is considered here as a colour based pixel clustering problem. Lymphocyte image segmentation algorithm developed here labels each pixel into nucleus, cytoplasm or background region based on the nearness measure.