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Proceedings Paper

3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning
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Paper Abstract

We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.

Paper Details

Date Published: 17 February 2012
PDF: 9 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83162O (17 February 2012); doi: 10.1117/12.912188
Show Author Affiliations
Xiaofeng Yang, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8316:
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes III; Kenneth H. Wong, Editor(s)

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