Automated ovarian follicle recognition for Polycystic Ovary Syndrome

Abstract

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting many women in the pubertal as well as reproductive age groups with profound adverse affects such as obesity, infertility, cardiovascular disease and diabetes mellitus. Diagnosis of the condition is by clinical, biochemical and imaging parameters. The principle feature on ultrasound is the presence of polycystic ovaries with peripheral arranged cysts and dense stroma. During ultrasound evaluation due to overlapping of the follicles as well as inherent noise of the equipment delineating, making this characteristic appearance may sometimes become challenging, making diagnosis time consuming. Moreover the interpretation would vary considerably from one operator to another as it is largely an experience dependent procedure. In this paper an automated scheme for the detection of this pathognomonic pattern and arrangement of follicles is proposed to overcome this problem. Firstly the input ultrasound image was preprocessed by multiscale morphological approach for contrast enhancement. Then a scanline thresholding is used to extract the contours of the follicles. The results are compared with the results obtained by manual selection to verify the effectivity of scheme.