Fuzzy inference system for follicle detection in ultrasound images of ovaries

The ovarian ultrasound imaging is an effective tool in infertility treatment. Monitoring the follicles is especially important in human reproduction. Periodic measurements of the size and shape of follicles over several days are the primary means of evaluation by physicians. Today monitoring the follicles is done by non-automatic means with human interaction. This work can be very demanding and inaccurate and, in most of the cases, means only an additional burden for medical experts. To improve the performance of follicle detection in ultrasound images of ovaries, we develop a new algorithm using fuzzy logic. The proposed method employs contourlet transform for despeckling the ultrasound images of ovaries, active contours without edge method for segmentation and fuzzy logic for classification. The follicles in an ovary are characterized by seven geometric features which are used as inputs to the fuzzy logic block of the Fuzzy Inference System. The output of the fuzzy logic block is a follicle class or non follicle class. The fuzzy-knowledge-base consists of a set of physically interpretable if-then rules providing physical insight into the process. The experimentation has been done using sample ultrasound images of ovaries and the results are compared with the inferences drawn by interval based classifier and also those drawn by the medical expert. The experimental results demonstrate the efficacy of the proposed method.