ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Texture guided active appearance model propagation for prostate segmentation
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Prostate segmentation in 2d ultrasound images using image warping and ellipse fitting
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Computer Methods and Programs in Biomedicine
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Low contrast of the prostate gland, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow regions, speckle and significant variations in prostate shape, size and inter dataset contrast in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a probabilistic framework for automatic initialization and propagation of multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. Unlike traditional statistical models of shape and intensity priors we use posterior probability of the prostate region to build our texture model of the prostate and use the information in initialization and propagation of the mean model. Furthermore, multiple mean models are used compared to a single mean model to improve segmentation accuracies. The proposed method achieves mean Dice Similarity Coefficient (DSC) value of 0.97±0.01, and mean Mean Absolute Distance (MAD) value of 0.49±0.20 mm when validated with 23 datasets with considerable shape, size, and intensity variations, in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value