eprintid: 20327
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/03/27
datestamp: 2025-11-06 03:46:10
lastmod: 2025-11-06 03:46:10
status_changed: 2025-11-06 03:46:10
type: article
metadata_visibility: show
creators_name: Mustafa, Wan Azani
creators_name: Khiruddin, Khalis
creators_name: Saidi, Syahrul Affandi
creators_name: Jamaludin, Khairur Rijal
creators_name: Hakimi, Halimaton
creators_name: Jamlos, Mohd Aminudin
title: Automated Cervical Nuclei Segmentation in Pap Smear Images Using Enhanced Morphological Thresholding Techniques
ispublished: pub
keywords: accuracy; adult; algorithm; Article; benchmarking; cancer screening; carcinoma in situ; cell nucleus; cell separation; contrast enhancement; controlled study; diagnostic error; diagnostic test accuracy study; diagnostic value; dynamic contrast enhanced imaging; dysplasia; female; Herpes simplex virus; human; image analysis; image processing; image quality; image segmentation; Papanicolaou test; performance indicator; quality control; segmentation algorithm; uterine cervix cancer
note: Cited by: 0; All Open Access, Gold Open Access, Green Open Access
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 cervical cancer screening and offers a foundation for broader applications in medical image analysis. © 2025 by the authors.
date: 2025
publisher: Multidisciplinary Digital Publishing Institute (MDPI)
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017392248&doi=10.3390%2fdiagnostics15182328&partnerID=40&md5=4260b26444fe89ce90e58482d2974ee5
id_number: 10.3390/diagnostics15182328
full_text_status: none
publication: Diagnostics
volume: 15
number: 18
refereed: TRUE
issn: 20754418
citation:   Mustafa, Wan Azani and Khiruddin, Khalis and Saidi, Syahrul Affandi and Jamaludin, Khairur Rijal and Hakimi, Halimaton and Jamlos, Mohd Aminudin  (2025) Automated Cervical Nuclei Segmentation in Pap Smear Images Using Enhanced Morphological Thresholding Techniques.  Diagnostics, 15 (18).   ISSN 20754418