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In this paper, a computerized approach to segmenting prostate lesions in transrectal ultrasound (TRUS) images is presented. The segmentation of prostate lesions from TRUS images is very challenging due to issues, such as poor contrast, low SNRs, and irregular shape variations. To address these issues, a novel approach is employed to segment the lesions from the surrounding prostate, where region merging is performed via a region-merging likelihood function based on regional statistics, as well as Fisher-Tippett statistics. Experimental results using TRUS prostate images demonstrate that the proposed Fisher-Tippett region-merging approach achieves more accurate segmentation of prostate lesions when compared to other segmentation methods.