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1 March 2011 Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior
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Abstract
We are developing a molecular image-directed, 3D ultrasound-guided, targeted biopsy system for improved detection of prostate cancer. In this paper, we propose an automatic 3D segmentation method for transrectal ultrasound (TRUS) images, which is based on multi-atlas registration and statistical texture prior. The atlas database includes registered TRUS images from previous patients and their segmented prostate surfaces. Three orthogonal Gabor filter banks are used to extract texture features from each image in the database. Patient-specific Gabor features from the atlas database are used to train kernel support vector machines (KSVMs) and then to segment the prostate image from a new patient. The segmentation method was tested in TRUS data from 5 patients. The average surface distance between our method and manual segmentation is 1.61 ± 0.35 mm, indicating that the atlas-based automatic segmentation method works well and could be used for 3D ultrasound-guided prostate biopsy.
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Xiaofeng Yang, David Schuster, Viraj Master, Peter Nieh, Aaron Fenster, and Baowei Fei "Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior", Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 796432 (1 March 2011); https://doi.org/10.1117/12.877888
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