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

Roopa B. Hegde, Keerthana Prasad, Harishchandra Hebbar, Brij Mohan Kumar Singh

    Research output: Contribution to journalArticle

    7 Citations (Scopus)

    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.

    Original languageEnglish
    Article number110
    JournalJournal of Medical Systems
    Volume42
    Issue number6
    DOIs
    Publication statusPublished - 01-06-2018

    Fingerprint

    Leukocytes
    Blood
    Cells
    Basophils
    Lymphocytes
    Color
    Image Enhancement
    Lighting
    Image enhancement
    Malaria
    Anemia
    Lymphoma
    Blood Cells
    Cytoplasm
    Leukemia
    Image processing
    Classifiers
    Textures
    Neural networks
    Infection

    All Science Journal Classification (ASJC) codes

    • Medicine (miscellaneous)
    • Information Systems
    • Health Informatics
    • Health Information Management

    Cite this

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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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    Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images. / Hegde, Roopa B.; Prasad, Keerthana; Hebbar, Harishchandra; Singh, Brij Mohan Kumar.

    In: Journal of Medical Systems, Vol. 42, No. 6, 110, 01.06.2018.

    Research output: Contribution to journalArticle

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    AU - Hegde, Roopa B.

    AU - Prasad, Keerthana

    AU - Hebbar, Harishchandra

    AU - Singh, Brij Mohan Kumar

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