Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection

PurposeMicroscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation.MethodsAccurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images.ResultsComparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction.ConclusionsIn this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme.

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