DocumentCode :
1291904
Title :
Fisher–Tippett Region-Merging Approach to Transrectal Ultrasound Prostate Lesion Segmentation
Author :
Wong, Alexander ; Scharcanski, Jacob
Author_Institution :
Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada
Volume :
15
Issue :
6
fYear :
2011
Firstpage :
900
Lastpage :
907
Abstract :
In this paper, a computerized approach to segmenting prostate lesions in transrectal ultrasound (TRUS) images is presented. The segmentation of prostate lesions from TRUS images is very challenging due to issues, such as poor contrast, low SNRs, and irregular shape variations. To address these issues, a novel approach is employed to segment the lesions from the surrounding prostate, where region merging is performed via a region-merging likelihood function based on regional statistics, as well as Fisher-Tippett statistics. Experimental results using TRUS prostate images demonstrate that the proposed Fisher-Tippett region-merging approach achieves more accurate segmentation of prostate lesions when compared to other segmentation methods.
Keywords :
biomedical ultrasonics; cancer; image segmentation; medical image processing; statistical analysis; Fisher-Tippett region-merging approach; Fisher-Tippett statistics; TRUS prostate images; computerized approach; prostate lesion; region-merging likelihood function; transrectal ultrasound image segmentation; transrectal ultrasound prostate lesion segmentation; Cancer; Image segmentation; Lesions; Noise measurement; Prostate cancer; Ultrasonic imaging; Fisher–Tippett; lesion; prostate cancer; region merging; Adenocarcinoma; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Male; Models, Statistical; Pattern Recognition, Automated; Prostate; Prostatic Neoplasms; Rectum; Reproducibility of Results; Signal-To-Noise Ratio; Ultrasonography;
fLanguage :
English
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-7771
Type :
jour
DOI :
10.1109/TITB.2011.2163724
Filename :
5976450
Link To Document :
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