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Proceedings Paper

Prostate segmentation in ultrasound images with deformable shape priors
Author(s): Lixin Gong; Sayan Dev Pathak; David R. Haynor; Paul S. Cho; Yongmin Kim
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Paper 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.

Paper Details

Date Published: 30 May 2003
PDF: 9 pages
Proc. SPIE 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display, (30 May 2003); doi: 10.1117/12.480384
Show Author Affiliations
Lixin Gong, Univ. of Washington (United States)
Sayan Dev Pathak, Univ. of Washington (United States)
David R. Haynor, Univ. of Washington (United States)
Paul S. Cho, Univ. of Washington (United States)
Yongmin Kim, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 5029:
Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display
Robert L. Galloway, Editor(s)

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