9 May 2002 Automatic segmentation of prostate boundaries in transrectal ultrasound (TRUS) imaging
Author Affiliations +
Proceedings Volume 4684, Medical Imaging 2002: Image Processing; (2002); doi: 10.1117/12.467183
Event: Medical Imaging 2002, 2002, San Diego, California, United States
Abstract
An automatic segmentation method for detecting the prostate boundary in transrectal ultrasound (TRUS) images was developed. The TRUS images were preprocessed by using an adaptive directional filtering and an automatic attenuation compensation for noise removal and contrast enhancement. A directional search strategy was used to locate key-points on the prostate boundary. The prostate contour was interpolated from the key-points under the supervision of a morphological prostate boundary model, which had been trained from prior manual segmentation of a large number of TRUS images. A new prostate center was calculated based on the intermediate segmentation result. The algorithm is reiterated until the prostate boundary and center reach a stable state. The overall performance of the method was compared to manual segmentation of an expert radiologist. About 78% out of 282 TRUS images (excluding base and apex slices) from three types of ultrasound machine (Acuson, Siemens, and B&K) were correctly delineated. The segmentation error was 0.9 mm averaged on 30 selected images, 10 for each type of machine. The computation time for a typical series of TRUS images is approximately 1 minute on a Pentium-II computer.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haisong Liu, Gang Cheng, Deborah Rubens, John G. Strang, Lydia Liao, Ralph Brasacchio, Edward Messing, Yan Yu, "Automatic segmentation of prostate boundaries in transrectal ultrasound (TRUS) imaging", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467183; https://doi.org/10.1117/12.467183
PROCEEDINGS
12 PAGES


SHARE
KEYWORDS
Prostate

Image segmentation

Ultrasonography

Edge detection

Image filtering

Signal attenuation

Digital filtering

Back to Top