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Proceedings Paper

3D segmentation of prostate ultrasound images using wavelet transform
Author(s): Hamed Akbari; Xiaofeng Yang; Luma V. Halig; Baowei Fei
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Paper Abstract

The current definitive diagnosis of prostate cancer is transrectal ultrasound (TRUS) guided biopsy. However, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. This paper presents a new method for automatic segmentation of the prostate in three-dimensional transrectal ultrasound images, by extracting texture features and by statistically matching geometrical shape of the prostate. A set of Wavelet-based support vector machines (WSVMs) are located and trained at different regions of the prostate surface. The WSVMs capture texture priors of ultrasound images for classification of the prostate and non-prostate tissues in different zones around the prostate boundary. In the segmentation procedure, these W-SVMs are trained in three sagittal, coronal, and transverse planes. The pre-trained W-SVMs are employed to tentatively label each voxel around the surface of the model as a prostate or non-prostate voxel by the texture matching. The labeled voxels in three planes after post-processing is overlaid on a prostate probability model. The probability prostate model is created using 10 segmented prostate data. Consequently, each voxel has four labels: sagittal, coronal, and transverse planes and one probability label. By defining a weight function for each labeling in each region, each voxel is labeled as a prostate or non-prostate voxel. Experimental results by using real patient data show the good performance of the proposed model in segmenting the prostate from ultrasound images.

Paper Details

Date Published: 14 March 2011
PDF: 6 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622K (14 March 2011); doi: 10.1117/12.878072
Show Author Affiliations
Hamed Akbari, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)
Luma V. Halig, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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