Prostate segmentation in ultrasound images with deformable shape priors

Lixin Gong; Sayan Dev Pathak; David R. Haynor; Paul S. Cho; Yongmin Kim

doi:10.1117/12.480384

30 May 2003 Prostate segmentation in ultrasound images with deformable shape priors

Lixin Gong, Sayan Dev Pathak, David R. Haynor, Paul S. Cho, Yongmin Kim

Proceedings Volume 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display; (2003) https://doi.org/10.1117/12.480384
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Lixin Gong, Sayan Dev Pathak, David R. Haynor, Paul S. Cho, and 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); https://doi.org/10.1117/12.480384

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