Background:
In this paper a novel method for prostate segmentation in transrectal ultrasound images is presented.

Methods:
A segmentation procedure consisting of four main stages is proposed. In the first stage,a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation.

Results:
A total average similarity of 98.76%(± 0.68) with gold standards was achieved.

Conclusion:
The proposed approach represents a robust and accurate approach to prostate segmentation.

Reprinted with permission from Biomedical Engineering Online