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Author Notes:

Corresponding author: bfei@emory.edu


Research Funding:

This research is supported in part by NIH grant R01CA156775 (PI: Fei), Coulter Translational Research Grant (Pis: Fei and Hu), Georgia Cancer Coalition Distinguished Clinicians and Scientists Award (PI: Fei), Emory Molecular and Translational Imaging Center (NIH P50CA128301), and Atlanta Clinical and Translational Science Institute (ACTSI) that is supported by the PHS Grant UL1 RR025008 from the Clinical and Translational Science Award program.


  • Automatic 3D segmentation
  • atlas registration
  • Gabor filter
  • support vector machine
  • ultrasound imaging
  • prostate cancer

Automatic 3D Segmentation of Ultrasound Images Using Atlas Registration and Statistical Texture Prior


Journal Title:

Proceedings of SPIE


Volume 7964


, Pages 1-9

Type of Work:

Article | Post-print: After Peer Review


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.

Copyright information:

© 2011 SPIE

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