Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 16, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

533

Automated Cervical Nuclei Segmentation in Pap Smear Images Using Enhanced Morphological Thresholding Techniques.

Wan Azani Mustafa1,2, Khalis Khiruddin1, Syahrul Affandi Saidi1

  • 1Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Perlis, Malaysia.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Oleoresin Extraction from Zingiber officinale Using Hot Compressed Water Extraction.

Journal of oleo science·2026
Same author

Comparative analysis of cervical cancer classification of DPAGCHE-enhanced Pap smear images using convolutional neural network models.

PloS one·2025
Same author

Editorial for the Special Issue "Advances in Medical Image Processing, Segmentation, and Classification".

Diagnostics (Basel, Switzerland)·2025
Same author

Editorial for the Special Issue "Medical Data Processing and Analysis-2nd Edition".

Diagnostics (Basel, Switzerland)·2025
Same author

Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model.

Bioengineering (Basel, Switzerland)·2023
Same author

A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images.

Diagnostics (Basel, Switzerland)·2023
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

This study presents an improved algorithm for segmenting cervical cell nuclei in Pap smear images, enhancing accuracy for automated screening and aiding early cervical cancer detection.

Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Biomedical Engineering

Background:

  • Cervical cancer is a leading global cause of death, especially where screening access is limited.
  • Manual Pap smear analysis is subjective and prone to errors, necessitating automated solutions.
  • Accurate segmentation of cervical cell nuclei is crucial for automated analysis but challenging due to image artifacts.

Purpose of the Study:

  • To develop an improved algorithm for accurate cervical nucleus segmentation.
  • To support automated Pap smear analysis and enhance diagnostic reliability.
  • To address challenges like overlapping cells, poor contrast, and staining variability.

Main Methods:

  • Adaptive gamma correction for contrast enhancement.
Keywords:
adaptive morphologicalimage quality assessmentnucleus segmentation

More Related Videos

Isolation of Cardiomyocytes from Fixed Hearts for Immunocytochemistry and Ploidy Analysis
08:41

Isolation of Cardiomyocytes from Fixed Hearts for Immunocytochemistry and Ploidy Analysis

Published on: October 7, 2020

7.6K
Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

624

Related Experiment Videos

Last Updated: Jan 16, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

533
Isolation of Cardiomyocytes from Fixed Hearts for Immunocytochemistry and Ploidy Analysis
08:41

Isolation of Cardiomyocytes from Fixed Hearts for Immunocytochemistry and Ploidy Analysis

Published on: October 7, 2020

7.6K
Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

624
  • Otsu thresholding for initial segmentation.
  • Adaptive morphological operations for post-processing refinement.
  • Evaluation using image quality metrics and ground truth validation.

    • Main Results:

    • Achieved high performance with Precision (0.9965), F-measure (97.29%), and Accuracy (98.39%).
    • Demonstrated improved image clarity (PSNR 16.62) and sensitivity.
    • Showed effectiveness across varying cell overlaps and staining conditions, outperforming traditional methods.

    Conclusions:

    • The algorithm provides robust and accurate cervical nucleus segmentation for automated Pap smear analysis.
    • It offers a consistent framework for automated screening tools, enhancing diagnostic reliability.
    • This work lays the foundation for broader applications in medical image analysis.