Automated ovarian classification in digital ultrasound images

Abstract

Knowledge about the status of the female reproductive system is important for addressing fertility problems and age-related family planning. Transvaginal ultrasound imaging of the follicles in the ovary gives important information about the ovarian ageing, i.e. number of follicles, size, position and response to hormonal stimulation. Manual analysis of follicles is laborious and error-prone. In this paper, a novel method for automated classification of the ovaries in digital ultrasound images is proposed which employs the contourlet transform for pre-processing, active contours without edge for segmentation and fuzzy logic for classification. Further, upon the detection of the follicles, the ovary is classified as normal, cystic and polycystic, on the basis of two parameters, namely, the number and the size of follicles in an ovary. The experimental results are compared with inferences drawn by medical expert and demonstrate the efficacy of the method. © 2013 Inderscience Enterprises Ltd.