30 May 2003 Prostate segmentation in ultrasound images with deformable shape priors
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Abstract
Automated prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing edge segments, and complex prostate peripheral anatomy. In this paper, a Bayesian prostate segmentation algorithm is presented. It combines both prior shape and image information for robust segmentation. In this study, the prostate shape was efficiently modeled using deformable superellipse. A flexible graphical user interface has been developed to facilitate the validation of our algorithm in a clinical setting. This algorithm was applied to 66 ultrasound images collected from 8 patients. The resulting mean error between the computer-generated boundaries and the manually-outlined boundaries was 1.39 ± 0.60 mm, which is significantly less than the variability between human experts.
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Lixin Gong, Lixin Gong, Sayan Dev Pathak, Sayan Dev Pathak, David R. Haynor, David R. Haynor, Paul S. Cho, Paul S. Cho, Yongmin Kim, Yongmin Kim, } "Prostate segmentation in ultrasound images with deformable shape priors", Proc. SPIE 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display, (30 May 2003); doi: 10.1117/12.480384; https://doi.org/10.1117/12.480384
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