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Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images

  • Image & Signal Processing
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Abstract

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei. Dice similarity coefficient of 0.95 was obtained for nucleus detection. We also compared the proposed method with a state-of-the-art method and the proposed method was found to be better. Shape and texture features of the detected nuclei were used for classifying white blood cells. We considered classification of WBCs using two approaches such as 5-class and cell-by-cell approaches using neural network and hybrid-classifier respectively. We compared the results of both the approaches for classification of white blood cells. Cell-by-cell approach offered 1.4% higher sensitivity in comparison with the 5-class approach. We obtained an accuracy of 100% for lymphocyte and basophil detection. Hence, we conclude that lymphocytes and basophils can be accurately detected even when the analysis is limited to the features of nuclei whereas, accurate detection of other types of WBCs will require analysis of the cytoplasm too.

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Authors and Affiliations

  1. School of Information Sciences, MAHE, Manipal, 576104, India

    Roopa B. Hegde, Keerthana Prasad & Harishchandra Hebbar

  2. Department of Pathology, Kasturba Medical College, MAHE, Manipal, Karnataka, 574106, India

    Brij Mohan Kumar Singh

Authors
  1. Roopa B. Hegde
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  2. Keerthana Prasad
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  3. Harishchandra Hebbar
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  4. Brij Mohan Kumar Singh
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Correspondence to Keerthana Prasad.

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The authors declare that they have no conflict of interest. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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This article is part of the Topical Collection on Image & Signal Processing

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Hegde, R.B., Prasad, K., Hebbar, H. et al. Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images. J Med Syst 42, 110 (2018). https://doi.org/10.1007/s10916-018-0962-1

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  • DOI: https://doi.org/10.1007/s10916-018-0962-1

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