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All Journal Bulletin of Electrical Engineering and Informatics
I. N. A. M. Nordin
Universiti Tun Hussein Onn Malaysia
Electrical & Electronics Engineering

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Review on automated follicle identification for polycystic ovarian syndrome A A Nazarudin; Noraishikin Zulkarnain; A. Hussain; S. S. Mokri; I. N. A. M. Nordin
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (412.417 KB) | DOI: 10.11591/eei.v9i2.2089

Abstract

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.
Co-Authors A A Nazarudin A. Hussain Noraishikin Zulkarnain S. S. Mokri