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An effective automated system in follicle identification for Polycystic Ovary Syndrome using ultrasound images

Polycystic Ovary Syndrome is a common disorder which is caused by the formation of many follicles in the ovary. It seriously affects women's health. An automated diagnostic system is used to detect such follicles in the ovary region. The effective and automated method for the computer-aided diagnosis of PCOS using ovary ultrasound images is presented. The method is detecting the follicles using object growing. It consists of two major stages including preprocessing phase and follicle identification based on object growing. The speckle noise in the input PCOS ultrasound image is reduced using median filter. After reducing the speckle noise, the local minimum is extracted which represents the possible follicles using enhanced labeled watershed algorithm and the region of interest is selected to make the segmentation part an easier one. The object growing algorithm selects the objects that are likely to be follicles and thus the follicles are detected.