TY - GEN
T1 - Segmentation of prostate boundaries using regional contrast enhancement
AU - Sahba, Farhang
AU - Tizhoosh, Hamid R.
AU - Salama, Magdy M.A.
PY - 2005
Y1 - 2005
N2 - In this paper a novel method for automatic prostate segmentation in transrectal ultrasound images is presented. Morphological grey level transformations are first used to generate an image with enough bright intensity around the prostate. This image is then thresholded to produce a binary image. Then by finding and using a point as the inside point for the prostate, a Kalman estimator is used to isolate the prostate boundary from any irrelevant parts and produce a roughly segmented version (as coarse estimation). Consequently, a fuzzy inference system describing regional and gray level information is employed to enhance the contrast of the prostate with respect to the background. Using strong edges obtained from this enhanced image and information from pixels gradients and also the characteristics in the vicinity of the coarse estimation, the final boundary is extracted. A number of experiments are conducted to validate this method.
AB - In this paper a novel method for automatic prostate segmentation in transrectal ultrasound images is presented. Morphological grey level transformations are first used to generate an image with enough bright intensity around the prostate. This image is then thresholded to produce a binary image. Then by finding and using a point as the inside point for the prostate, a Kalman estimator is used to isolate the prostate boundary from any irrelevant parts and produce a roughly segmented version (as coarse estimation). Consequently, a fuzzy inference system describing regional and gray level information is employed to enhance the contrast of the prostate with respect to the background. Using strong edges obtained from this enhanced image and information from pixels gradients and also the characteristics in the vicinity of the coarse estimation, the final boundary is extracted. A number of experiments are conducted to validate this method.
UR - http://www.scopus.com/inward/record.url?scp=33749647721&partnerID=8YFLogxK
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U2 - 10.1109/ICIP.2005.1530293
DO - 10.1109/ICIP.2005.1530293
M3 - Conference contribution
AN - SCOPUS:33749647721
SN - 0780391349
SN - 9780780391345
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1266
EP - 1269
BT - IEEE International Conference on Image Processing 2005, ICIP 2005
T2 - IEEE International Conference on Image Processing 2005, ICIP 2005
Y2 - 11 September 2005 through 14 September 2005
ER -