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primary topic: Automated Cervical Nuclei Segmentation in Pap Smear Images Using Enhanced Morphological Thresholding Techniques

Automated Cervical Nuclei Segmentation in Pap Smear Images Using Enhanced Morphological Thresholding Techniques
type: AcademicArticle
type: Article
abstract: Background and Objective: Cervical cancer remains one of the leading causes of death among women worldwide, particularly in regions with limited access to early screening. Pap smear screening is the primary tool for early detection, but manual interpretation is labor-intensive, subjective, and prone to inconsistency and misdiagnosis. Accurate segmentation of cervical cell nuclei is essential for automated analysis but is often hampered by overlapping cells, poor contrast, and staining variability. This research aims to develop an improved algorithm for accurate cervical nucleus segmentation to support automated Pap smear analysis. Method: The proposed method involves a combination of adaptive gamma correction for contrast enhancement, followed by Otsu thresholding for segmentation. Post-processing is performed using adaptive morphological operations to refine the results. The system is evaluated using standard image quality assessment metrics and validated against ground truth annotations. Result: The results show a significant improvement in segmentation performance over conventional methods. The proposed algorithm achieved a Precision of 0.9965, an F-measure of 97.29, and an Accuracy of 98.39. The PSNR value of 16.62 indicates enhanced image clarity after preprocessing. The method also improved sensitivity, leading to better identification of nuclei boundaries. Advanced preprocessing techniques, including edge-preserving filters and multi-Otsu thresholding, contributed to more accurate cell separation. The segmentation method proved effective across varying cell overlaps and staining conditions. Comparative evaluations with traditional clustering methods confirmed its superior performance. Conclusions: The proposed algorithm delivers robust and accurate segmentation of cervical cell nuclei, addressing common challenges in Pap smear image analysis. It provides a consistent framework for automated screening tools. This work enhances diagnostic reliability in cervica…
date: 2025
volume: 15
issue: 18
is part of: Diagnostics
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Multidisciplinary Digital Publishing Institute (MDPI)
type: Organization

Diagnostics
type: Collection
issn: 20754418

Syahrul Affandi Saidi
type: Person
given name: Syahrul Affandi
family name: Saidi

Halimaton Hakimi
type: Person
given name: Halimaton
family name: Hakimi

Mohd Aminudin Jamlos
type: Person
given name: Mohd Aminudin
family name: Jamlos

Wan Azani Mustafa
type: Person
given name: Wan Azani
family name: Mustafa

Khalis Khiruddin
type: Person
given name: Khalis
family name: Khiruddin

Khairur Rijal Jamaludin
type: Person
given name: Khairur Rijal
family name: Jamaludin

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primary topic: Automated Cervical Nuclei Segmentation in Pap Smear Images Using Enhanced Morphological Thresholding Techniques