Review on automated follicle identification for polycystic ovarian syndrome

A A Nazarudin (Centre for Integrated Systems Engineering and Advanced Technologies (Integra), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor.)
Noraishikin Zulkarnain (Centre for Integrated Systems Engineering and Advanced Technologies (Integra), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor.)
A. Hussain (Centre for Integrated Systems Engineering and Advanced Technologies (Integra), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor.)
S. S. Mokri (Centre for Integrated Systems Engineering and Advanced Technologies (Integra), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor.)
I. N. A. M. Nordin (Universiti Tun Hussein Onn Malaysia)

Bulletin of Electrical Engineering and Informatics
Vol 9, No 2: April 2020

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. For 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.