Review on automated follicle identification for polycystic ovarian syndrome

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

Full text not available from this repository. (Request a copy)


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.

Item Type: Article
Uncontrolled Keywords: Automated segmentation; Follicle identification; Image segmentation; PCOS; Polycystic ovarian syndrome
Subjects: R Medicine > R Medicine (General) > R855-855.5 Medical technology
Divisions: Faculty of Engineering Technology > Department of Electrical Engineering Technology
Depositing User: Miss Afiqah Faiqah Mohd Hafiz
Date Deposited: 26 Jan 2022 06:48
Last Modified: 26 Jan 2022 06:48

Actions (login required)

View Item View Item