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Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring

Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing tec...

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Autores principales: Nazarudin, Asma’ Amirah, Zulkarnain, Noraishikin, Mokri, Siti Salasiah, Zaki, Wan Mimi Diyana Wan, Hussain, Aini, Ahmad, Mohd Faizal, Nordin, Ili Najaa Aimi Mohd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954948/
https://www.ncbi.nlm.nih.gov/pubmed/36832237
http://dx.doi.org/10.3390/diagnostics13040750
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author Nazarudin, Asma’ Amirah
Zulkarnain, Noraishikin
Mokri, Siti Salasiah
Zaki, Wan Mimi Diyana Wan
Hussain, Aini
Ahmad, Mohd Faizal
Nordin, Ili Najaa Aimi Mohd
author_facet Nazarudin, Asma’ Amirah
Zulkarnain, Noraishikin
Mokri, Siti Salasiah
Zaki, Wan Mimi Diyana Wan
Hussain, Aini
Ahmad, Mohd Faizal
Nordin, Ili Najaa Aimi Mohd
author_sort Nazarudin, Asma’ Amirah
collection PubMed
description Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu’s thresholding with the Chan–Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu’s thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan–Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan–Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan–Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan–Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu’s thresholding and the Chan–Vese method enhanced the segmentation of ultrasound images.
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spelling pubmed-99549482023-02-25 Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring Nazarudin, Asma’ Amirah Zulkarnain, Noraishikin Mokri, Siti Salasiah Zaki, Wan Mimi Diyana Wan Hussain, Aini Ahmad, Mohd Faizal Nordin, Ili Najaa Aimi Mohd Diagnostics (Basel) Article Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu’s thresholding with the Chan–Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu’s thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan–Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan–Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan–Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan–Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu’s thresholding and the Chan–Vese method enhanced the segmentation of ultrasound images. MDPI 2023-02-16 /pmc/articles/PMC9954948/ /pubmed/36832237 http://dx.doi.org/10.3390/diagnostics13040750 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nazarudin, Asma’ Amirah
Zulkarnain, Noraishikin
Mokri, Siti Salasiah
Zaki, Wan Mimi Diyana Wan
Hussain, Aini
Ahmad, Mohd Faizal
Nordin, Ili Najaa Aimi Mohd
Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
title Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
title_full Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
title_fullStr Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
title_full_unstemmed Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
title_short Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
title_sort performance analysis of a novel hybrid segmentation method for polycystic ovarian syndrome monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954948/
https://www.ncbi.nlm.nih.gov/pubmed/36832237
http://dx.doi.org/10.3390/diagnostics13040750
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