Research Papers

Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland

[+] Author Affiliations
Shahab Chitchian

University of North Carolina at Charlotte, Department of Physics and Optical Science, 9201 University City Boulevard, Charlotte, North Carolina 28223

Thomas P. Weldon

University of North Carolina at Charlotte, Department of Electrical and Computer Engineering, 9201 University City Boulevard, Charlotte, North Carolina 28223

Nathaniel M. Fried

University of North Carolina at Charlotte, Department of Physics and Optical Science, 9201 University City Boulevard, Charlotte, North Carolina 28223 and Johns Hopkins Medical Institutions, Department of Urology, 600 North Wolfe Street, Baltimore, Maryland 21287

J. Biomed. Opt. 14(4), 044033 (August 25, 2009). doi:10.1117/1.3210767
History: Received February 25, 2009; Revised June 12, 2009; Accepted June 23, 2009; Published August 25, 2009
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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.

Figures in this Article
© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Shahab Chitchian ; Thomas P. Weldon and Nathaniel M. Fried
"Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland", J. Biomed. Opt. 14(4), 044033 (August 25, 2009). ; http://dx.doi.org/10.1117/1.3210767


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