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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9954948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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