Segmentation Algorithms in Cervical Cancer Image Analysis

Author(s)	Mr. Vedant Vilas Lambade, Ms. Khushi Vijay Khanorkar, Ms. Riya Wamanrao Thombre, Mr. Ritesh Harihar Gajbhiye, Ms. Shruti Vanit Waghmare, Mr. Pushkar Nitin Dahikar, Dr. Kavita. R. Singh
Country	India
Abstract	Cervical cancer remains a major global health issue, particularly in developing regions where diagnostic resources are limited. This research presents a comparative study of two traditional image segmentation methods—Adaptive Thresholding and HSV-Morphological Cervical Segmentation—for preprocessing and analysis of colposcopy images. The Adaptive Thresholding approach integrates Gaussian blurring and CLAHE for enhanced contrast and local binarization, while the HSV-Morphological method combines color space transformation with morphological refinement to accurately extract the cervical region. Experimental results demonstrate that the HSV-Morphological approach achieves smoother and more precise segmentation, offering a resource-efficient preprocessing technique for deep learning-based cervical cancer screening.
Keywords	Cervical Cancer, Image Segmentation, Adaptive Thresholding, HSV Morphological Processing, Image Preprocessing, Medical Image Analysis
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 6, November-December 2025
Published On	2025-11-24
DOI	https://doi.org/10.63363/aijfr.2025.v06i06.1645
Short DOI	https://doi.org/hbdsw2

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