Share Email Print

Journal of Biomedical Optics • Open Access

Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland
Author(s): Shahab Chitchian; Thomas P. Weldon; Nathaniel M. Fried

Paper Abstract

The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. Two-dimensional (2-D) optical coherence tomography (OCT) images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. To detect these nerves, three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The Gabor feature was applied with different standard deviations in the x and y directions. In the Daubechies wavelet feature, an 8-tap Daubechies orthonormal wavelet was implemented, and the low-pass sub-band was chosen as the filtered image. Last, Laws feature extraction was applied to the images. The features were segmented using a nearest-neighbor classifier. N-ary morphological postprocessing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058±0.019. This algorithm may be useful for implementation in clinical endoscopic OCT systems currently being studied for potential intraoperative diagnostic use in laparoscopic and robotic nerve-sparing prostate cancer surgery.

Paper Details

Date Published: 1 July 2009
PDF: 4 pages
J. Biomed. Opt. 14(4) 044033 doi: 10.1117/1.3210767
Published in: Journal of Biomedical Optics Volume 14, Issue 4
Show Author Affiliations
Shahab Chitchian, The Univ. of North Carolina at Charlotte (United States)
Thomas P. Weldon, The Univ. of North Carolina at Charlotte (United States)
Nathaniel M. Fried, The Univ. of North Carolina at Charlotte (United States)

© SPIE. Terms of Use
Back to Top