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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Mdpi
2023
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| Online Access: | http://eprints.uthm.edu.my/8934/ http://eprints.uthm.edu.my/8934/1/J15891_a3d347f8edddeb01c372c95d56a57036.pdf |
| _version_ | 1848889537582858240 |
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| 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 |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T20:27:45Z |
| format | Article |
| id | uthm-8934 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:27:45Z |
| publishDate | 2023 |
| publisher | Mdpi |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-89342023-06-18T01:36:10Z http://eprints.uthm.edu.my/8934/ 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/8934/1/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 |
| 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 |
| 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 | T Technology (General) |
| url | http://eprints.uthm.edu.my/8934/ http://eprints.uthm.edu.my/8934/ http://eprints.uthm.edu.my/8934/1/J15891_a3d347f8edddeb01c372c95d56a57036.pdf |