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

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Abstract

Purpose

Microscopic 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.

Methods

Accurate 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.

Results

Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction.

Conclusions

In 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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Authors and Affiliations

  1. National Institute of Technology, Rourkela, 769008, India

    Subrajeet Mohapatra, Dipti Patra & Sunil Kumar

  2. Ispat General Hospital, Rourkela, 769005, India

    Sanghamitra Satpathy

Authors
  1. Subrajeet Mohapatra
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  2. Dipti Patra
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  3. Sunil Kumar
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  4. Sanghamitra Satpathy
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Correspondence to Subrajeet Mohapatra.

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Mohapatra, S., Patra, D., Kumar, S. et al. Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection. Biomed. Eng. Lett. 2, 100–110 (2012). https://doi.org/10.1007/s13534-012-0056-9

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  • DOI: https://doi.org/10.1007/s13534-012-0056-9

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