Machine Learning Assisted Medical Diagnosis for Segmentation of Follicle in Ovary Ultrasound

Abstract

Machine learning can be applied to the diagnosis of polycystic ovarian syndrome (PCOS), one of criteria for PCOS patients is presence polycystic ovary (PCO). PCO is the presence of the least 12 follicles in the ovary or follicular diameter between 2 and 9 mm and/or increased ovarian volume 10 cm3. In this research, a computational model for the detection of follicles of various sizes and extracting relevant features of the follicle and calculate the diameter and the number of follicles is proposed. The proposed model consists of pre-processing, speckle noise reduction, follicular segmentation, feature extraction, feature selection, and calculate the diameter of number follicles. The segmentation method uses active contour to divide objects base on the similarity of follicle shape feature so that it is more accurate in calculating the number and diameter of follicles. The performance of this method is tested on a dataset of ovarian ultrasound images of patients at Sardjito Hospital, Yogyakarta using Probabilistic Rand Index (PRI) and Global Consistency Error (GCE).