Automated computer-assisted detection of follicles in ultrasound images of ovary

Monitoring follicles is especially important in human reproduction. Today, the monitoring of follicles is done non-automatic, with human interaction. This work can be very demanding and inaccurate, and in most cases means only an additional burden for the experts. In this paper, new algorithm for automated computer-assisted defection of follicles in ultrasound images of ovary is proposed. It has typical object recognition scheme (preprocessing, segmentation and classification). The algorithm is assembled on the following idea: first, the ovary is estimated (coarse) and then follicles are searched. The methods used are known from literature (despeckle filter, Kirsh´s operator, optimal thresholding, thinning shape descriptions), the majority of the work was done experimenting with these methods and selecting the appropriate thresholds. The algorithms computational complexity is of order O(n/sup 2/), which means about 6 minutes of processing time per ultrasound image of dimensions of 768/spl times/576 pixels (on HP 715 machines). Algorithm is not perfect, but it can be easily modified and improved. Recognition rate of follicles with these procedure is around 70%.