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.
Highlights
Polycystic Ovarian Syndrome, -or commonly known as PCOS is described as a condition of numerous primordial follicles in the ovary
This disorder is diagnosed by ultrasound imaging which gives important information on the ovarian follicle quantity and size
Ovarian ultrasound imaging has become an effective tool in PCOS diagnosis and ovarian follicle identification
Summary
Polycystic Ovarian Syndrome, -or commonly known as PCOS is described as a condition of numerous primordial follicles in the ovary. In developing an automated follicle identification for PCOS diagnosis system, the follicles are the regions of interest (ROIs) in an ovarian ultrasound image, which need to be detected using image processing techniques. Based on previous comparative studies in automated follicle identificat ion for PCOS diagnosis [2,3, 8], researchers concluded it is needed to develop algorithm in removing the speckle noise in the ultrasound image effectively and to investigate features extraction for classification of the ovary. This paper objective is to review previous methods of image segmentation in ultrasound image for automated follicle identification for PCOS diagnosis; the methods applied and the parameter for evaluation. To study the image segmentation methods applied by previous researchers, this paper applied snowball method This method had focused on the literature regarding automated follicles identification in ovarian ultrasound images.
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