Review on automated follicle identification for polycystic ovarian syndrome
Polycystic Ovarian Syndrome (PCOS), is a condition of the ovary consisting numerous follicles. Accurate size and number of follicles detected are crucial for treatment. Hence the diagnosis of this condition is by measuring and calculating the size and number of follicles existed in the ovary. To dia...
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Universitas Ahmad Dahlan
2020
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| Online Access: | http://eprints.uthm.edu.my/6095/ https://dx.doi.org/10.11591/eei.v9i2.2089 |
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| author | Nazarudin, A. A. Zulkarnain, N. Hussain, A. Mokri, S. S. Mohd Nordin, I. N. A. |
| author_facet | Nazarudin, A. A. Zulkarnain, N. Hussain, A. Mokri, S. S. Mohd Nordin, I. N. A. |
| author_sort | Nazarudin, A. A. |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Polycystic Ovarian Syndrome (PCOS), is a condition of the ovary consisting numerous follicles. Accurate size and number of follicles detected are crucial for treatment. Hence the diagnosis of this condition is by measuring and calculating the size and number of follicles existed in the ovary. To diagnosis, ultrasound imaging has become an effective tool as it is non invasive, inexpensive and portable. However, the presence of speckle noise in ultrasound imaging has caused an obstruction for manual diagnosis which are high time consumption and often produce errors. Thus, image segmentation for ultrasound imaging is critical to identify follicles for PCOS diagnosis and proper health treatment. This paper presents different methods proposed and applied in automated follicle identification for PCOS diagnosis by previous researchers. In this paper, the methods and performance evaluation are identified and compared. Finally, this paper also provided suggestions in developing methods for future research. |
| format | Article |
| id | my.uthm.eprints-6095 |
| institution | Universiti Tun Hussein Onn Malaysia |
| publishDate | 2020 |
| publisher | Universitas Ahmad Dahlan |
| record_format | eprints |
| spelling | my.uthm.eprints-60952022-01-26T06:48:34Z http://eprints.uthm.edu.my/6095/ Review on automated follicle identification for polycystic ovarian syndrome Nazarudin, A. A. Zulkarnain, N. Hussain, A. Mokri, S. S. Mohd Nordin, I. N. A. R855-855.5 Medical technology Polycystic Ovarian Syndrome (PCOS), is a condition of the ovary consisting numerous follicles. Accurate size and number of follicles detected are crucial for treatment. Hence the diagnosis of this condition is by measuring and calculating the size and number of follicles existed in the ovary. To diagnosis, ultrasound imaging has become an effective tool as it is non invasive, inexpensive and portable. However, the presence of speckle noise in ultrasound imaging has caused an obstruction for manual diagnosis which are high time consumption and often produce errors. Thus, image segmentation for ultrasound imaging is critical to identify follicles for PCOS diagnosis and proper health treatment. This paper presents different methods proposed and applied in automated follicle identification for PCOS diagnosis by previous researchers. In this paper, the methods and performance evaluation are identified and compared. Finally, this paper also provided suggestions in developing methods for future research. Universitas Ahmad Dahlan 2020 Article PeerReviewed Nazarudin, A. A. and Zulkarnain, N. and Hussain, A. and Mokri, S. S. and Mohd Nordin, I. N. A. (2020) Review on automated follicle identification for polycystic ovarian syndrome. Bulletin of Electrical Engineering and Informatics, 9 (2). pp. 588-593. ISSN 2089-3191 https://dx.doi.org/10.11591/eei.v9i2.2089 |
| spellingShingle | R855-855.5 Medical technology Nazarudin, A. A. Zulkarnain, N. Hussain, A. Mokri, S. S. Mohd Nordin, I. N. A. Review on automated follicle identification for polycystic ovarian syndrome |
| title | Review on automated follicle identification for polycystic ovarian syndrome |
| title_full | Review on automated follicle identification for polycystic ovarian syndrome |
| title_fullStr | Review on automated follicle identification for polycystic ovarian syndrome |
| title_full_unstemmed | Review on automated follicle identification for polycystic ovarian syndrome |
| title_short | Review on automated follicle identification for polycystic ovarian syndrome |
| title_sort | review on automated follicle identification for polycystic ovarian syndrome |
| topic | R855-855.5 Medical technology |
| url | http://eprints.uthm.edu.my/6095/ https://dx.doi.org/10.11591/eei.v9i2.2089 |
| url_provider | http://eprints.uthm.edu.my/ |