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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 te...

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Main Authors: Nazarudin, Asma’ Amirah, Zulkarnain, Noraishikin, Mokri, Siti Salasiah, Wan Zaki, Wan Mimi Diyana, Hussain, Aini, Ahmad, Mohd Faizal, Mohd Nordin, Ili Najaa Aimi
Format: Article
Language:English
Published: Mdpi 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/9818/1/J15891_a3d347f8edddeb01c372c95d56a57036.pdf
http://eprints.uthm.edu.my/9818/
https://doi.org/10.3390/diagnostics13040750
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spelling my.uthm.eprints.98182023-09-13T07:23:21Z http://eprints.uthm.edu.my/9818/ Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring Nazarudin, Asma’ Amirah Zulkarnain, Noraishikin Mokri, Siti Salasiah Wan Zaki, Wan Mimi Diyana Hussain, Aini Ahmad, Mohd Faizal Mohd Nordin, Ili Najaa Aimi T Technology (General) 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 Article PeerReviewed text en http://eprints.uthm.edu.my/9818/1/J15891_a3d347f8edddeb01c372c95d56a57036.pdf Nazarudin, Asma’ Amirah and Zulkarnain, Noraishikin and Mokri, Siti Salasiah and Wan Zaki, Wan Mimi Diyana and Hussain, Aini and Ahmad, Mohd Faizal and Mohd Nordin, Ili Najaa Aimi (2023) Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring. Diagnostics, 13 (750). pp. 1-18. https://doi.org/10.3390/diagnostics13040750
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Nazarudin, Asma’ Amirah
Zulkarnain, Noraishikin
Mokri, Siti Salasiah
Wan Zaki, Wan Mimi Diyana
Hussain, Aini
Ahmad, Mohd Faizal
Mohd Nordin, Ili Najaa Aimi
Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
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 Article
author Nazarudin, Asma’ Amirah
Zulkarnain, Noraishikin
Mokri, Siti Salasiah
Wan Zaki, Wan Mimi Diyana
Hussain, Aini
Ahmad, Mohd Faizal
Mohd Nordin, Ili Najaa Aimi
author_facet Nazarudin, Asma’ Amirah
Zulkarnain, Noraishikin
Mokri, Siti Salasiah
Wan Zaki, Wan Mimi Diyana
Hussain, Aini
Ahmad, Mohd Faizal
Mohd Nordin, Ili Najaa Aimi
author_sort Nazarudin, Asma’ Amirah
title 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_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_sort performance analysis of a novel hybrid segmentation method for polycystic ovarian syndrome monitoring
publisher Mdpi
publishDate 2023
url http://eprints.uthm.edu.my/9818/1/J15891_a3d347f8edddeb01c372c95d56a57036.pdf
http://eprints.uthm.edu.my/9818/
https://doi.org/10.3390/diagnostics13040750
_version_ 1778164199987347456
score 13.211869

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