| Summary: | Accurate segmentation of prostate region is an important prerequisite to improve the accuracy of computer-aided prostate cancer diagnosis. In this work, a new and accurate prostate segmentation algorithm is proposed and tested. The new algorithm consists of 4 steps: reading T2-weighted magnetic resonance images, calculating local binary pattern (LBP) feature map of prostate magnetic resonance images by using an 8x5 LBP feature template, segmenting the feature map with the improved distance regularization level set evolution (DRLSE) algorithm, and extracting coarse contour of the prostate. A new energy function is constructed to extract local gray scale information and gradient information, and the coarse contour is iteratively developed into the final fine prostate contour on the basis of this new energy function. The algorithm was tested with the SPIE-AAPM-NCI Prostate MR Classification Challenge Database. The segmentation results of the proposed algorithm were compared with that of manual segmentation by doctors. The results showed that the Dice coefficient obtained by using the proposed algorithm was 0.94±0.01, with a relative volume difference (RVD) of -1.21%±2.44% and a 95% Hausdorff distance (HD) of 6.15±0.66 mm. Compared with the existing segmentation algorithms, the segmentation results obtained with the algorithm proposed in this paper are closer to the manual segmentation results.
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