Automated Blood Cell Counting and Disease Identification Using Image Processing: Implications for Polymer Composite- Based Biomedical Diagnostic Devices

Year : 2025 | Volume : 13 | Special Issue 06 | Page : 262 270
    By

    Hari Krishnan G,

  • Sheeba Santhosh,

  • Mohandass G,

  • NMG Kumar,

  • Ram Prasad Reddy M,

  1. Associate Professor, Department of Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, India
  2. Associate Professor, Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
  3. Assistant Professor, Department of Biomedical Engineering, GRT Institute of Engineering and Technology, Tamil Nadu, India
  4. Professor, Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
  5. Professor, Department of Electrical and Electronics Engineering, Aditya College of Engineering, Madanapalle, Andhra Pradesh, India

Abstract

Accurate quantification of blood cells is central to clinical decision-making and to the performance of emerging polymer composite–based diagnostic platforms. This work presents a cost-effective, image-processing pipeline for automated counting of red blood cells (including overlapping cells), white blood cells, and platelets from Leishman-stained peripheral blood smears, and articulates its relevance to polymer composite microfluidic and biosensor devices. Implemented in Python with OpenCV, the workflow performs grayscale conversion, median/Gaussian denoising, contrast enhancement via CLAHE, edge detection, and Otsu thresholding; overlapping red cells are resolved using a Circular Hough Transform to recover individual instances. The acquisition setup—a compound microscope coupled to a consumer webcam—demonstrates accessibility without specialised haematology analysers. Validation on clinical smear images showed high accuracy and robustness, with automated counts consistent with reference ranges and capable of flagging deviations indicative of anaemia, infection, and platelet abnormalities. Beyond standalone laboratory analysis, we emphasise translation to polymer composite diagnostic devices: robust segmentation and reliable overlapping-cell resolution directly support on-chip quantification, device performance evaluation, and quality control in settings where composite optical and structural properties influence image formation. Overall, the proposed method advances affordable haematology while providing a practical pathway to integrate precise cellular analytics within polymer composite–based point-of-care platforms.

Keywords: Blood cell counting, image processing, overlapping RBC segmentation, disease identification, polymer composite biomedical devices, microfluidic diagnostics

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

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How to cite this article:
Hari Krishnan G, Sheeba Santhosh, Mohandass G, NMG Kumar, Ram Prasad Reddy M. Automated Blood Cell Counting and Disease Identification Using Image Processing: Implications for Polymer Composite- Based Biomedical Diagnostic Devices. Journal of Polymer and Composites. 2025; 13(06):262-270.
How to cite this URL:
Hari Krishnan G, Sheeba Santhosh, Mohandass G, NMG Kumar, Ram Prasad Reddy M. Automated Blood Cell Counting and Disease Identification Using Image Processing: Implications for Polymer Composite- Based Biomedical Diagnostic Devices. Journal of Polymer and Composites. 2025; 13(06):262-270. Available from: https://journals.stmjournals.com/jopc/article=2025/view=227130


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Special Issue Subscription Original Research
Volume 13
Special Issue 06
Received 26/08/2025
Accepted 05/09/2025
Published 15/09/2025
Publication Time 20 Days
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