Automatic segmentation of prostate boundaries from abdominal ultrasound images using priori knowledge

This article presents a method for automatic segmentation of prostate from abdominal freehand ultrasound images. A statistical model of prostate is estimated from manually delineated images. The segmentation starts by smoothing the image to enhance edges by applying a modified version of the adaptive filter which detects individual speckles and remove them, while it preserves valuable details. Then the boundary is initialized starting from the model and a simulated annealing optimization algorithm seeks the final form. The performances of the algorithm were compared with manual segmentation, the average distance was 31.7 pixels with a standard deviation of 2.3.