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Hematological image analysis for segmentation and characterization of erythrocytes using FC-TriSDR

  • Track 2: Medical Applications of Multimedia
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A Correction to this article was published on 05 October 2022

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Abstract

In medical science, the scrutiny of blood smears for the abnormality in erythrocyte, leads to decisive determination of several ailments like Thalasemia, Liver disease, Sickle cell anaemia and so on. The conventional methodology for determining the malformation of the erythrocytes is through visual inspection of the blood smear through light or compound microscope. Since, the process of such examination is manual, it might lead to discrepancies and subjectivity. It is a well-known fact that early and affordable diagnostics can make a significant impact on curative. Hence, this research study has proposed an image analysis perspective to characterize the erythrocytes based on their morphological changes. The prime objective of this research work is to enhance the preliminary screening of erythrocytes by analyzing the morphological, textural, and color features by the proposed model FC-TriSDR (Fuzzy C-Means clustering algorithm along with three ensembled classifiers- Support vector machine, Decision Tree, and Radial Basis Functional Neural Network). Automated identification and characterization of erythrocytes is accomplished by integrating the steps of image acquisition, preprocessing, sub-imaging, image segmentation, feature extraction, significant feature selection and classification into five different domains of erythrocytes as - Normal, Stomatocyte, Poikilocyte, Spherocyte, and Schistocyte in the Leishman stained microscopic blood smear images. Total 51 eminent features of erythrocyte were extracted and the performance of FC-TriSDR gained the highest accuracy of 96.7% with computational time of 1.68 sec. when compared amongst 5 other classic neural networks.

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Data Availability

The dataset generated and/or analysed during this research study will be made available from the corresponding author on reasonable request.

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  1. Department of Computer Science and Technology, Central University of Jharkhand, Ranchi, India

    Priyanka Kumar & Kanojia Sindhuben Babulal

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  1. Priyanka Kumar
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Correspondence to Kanojia Sindhuben Babulal.

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Kumar, P., Babulal, K.S. Hematological image analysis for segmentation and characterization of erythrocytes using FC-TriSDR. Multimed Tools Appl 82, 7861–7886 (2023). https://doi.org/10.1007/s11042-022-13613-5

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