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Comparative analysis evaluates region-based and watershed segmentation on blood smear images, highlighting performance trade-offs for computer-aided hematological diagnostics.
Image segmentation plays a significant role in medical image analysis by partitioning an image into meaningful regions that facilitate the identification and analysis of anatomical structures. It remains an active area of research due to the numerous challenges associated with accurately partitioning images into distinct regions. Accurate segmentation of blood smear images is essential for computer-aided diagnosis, disease detection, and hematological analysis. This study presents a comparative performance analysis of two widely used image segmentation techniques, namely Region-Based Segmentation and Watershed Segmentation, using blood smear images. The segmentation performance is quantitatively evaluated using standard metrics, including the Jaccard Coefficient, Dice Coefficient, True Positive Rate (TPR), and True Negative Rate (TNR).
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Mariena A A (2026) studied this question.
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