Automated detection of Polycystic Ovary Syndrome from ultrasound images

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder which seriously impacts women's health. The disorder is characterized by the formation of many follicular cysts in the ovary. Nowadays the diagnosis performed by doctors is to manually count the number of follicular cysts, which may lead to problems of the variability, reproducibility and low efficiency. To overcome these problems, an automated scheme is proposed to detect the PCOS. Firstly the input ovary ultrasound image is filtered by an adaptive morphological filter. Then a modified labeled watershed algorithm is used to extract contours of targets. Finally a clustering method is applied to identify expected follicular cysts. The experimental application verifies the effectivity of this proposed scheme, which achieves the accuracy rate of 84%.