OCT image segmentation of the prostate nerves

Shahab Chitchian; Thomas P. Weldon; Nathaniel M. Fried

doi:10.1117/12.825506

OCT image segmentation of the prostate nerves

Shahab Chitchian, Thomas P. Weldon, Nathaniel M. Fried

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. In this study, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. Three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The features were segmented using a nearestneighbor classifier. N-ary morphological post-processing 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.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Volume	7443
DOIs	https://doi.org/10.1117/12.825506
State	Published - 2009
Event	Applications of Digital Image Processing XXXII - San Diego, CA, United States Duration: Aug 3 2009 → Aug 5 2009

Other

Other	Applications of Digital Image Processing XXXII
Country/Territory	United States
City	San Diego, CA
Period	8/3/09 → 8/5/09

ASJC Scopus subject areas

Applied Mathematics
Computer Science Applications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Condensed Matter Physics

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10.1117/12.825506

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title = "OCT image segmentation of the prostate nerves",

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. In this study, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. Three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The features were segmented using a nearestneighbor classifier. N-ary morphological post-processing 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.",

author = "Shahab Chitchian and Weldon, {Thomas P.} and Fried, {Nathaniel M.}",

year = "2009",

doi = "10.1117/12.825506",

language = "English (US)",

isbn = "9780819477330",

volume = "7443",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

note = "Applications of Digital Image Processing XXXII ; Conference date: 03-08-2009 Through 05-08-2009",

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T1 - OCT image segmentation of the prostate nerves

AU - Chitchian, Shahab

AU - Weldon, Thomas P.

AU - Fried, Nathaniel M.

PY - 2009

Y1 - 2009

N2 - 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. In this study, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. Three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The features were segmented using a nearestneighbor classifier. N-ary morphological post-processing 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.

AB - 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. In this study, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. Three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The features were segmented using a nearestneighbor classifier. N-ary morphological post-processing 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.

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DO - 10.1117/12.825506

M3 - Conference contribution

AN - SCOPUS:70350418254

SN - 9780819477330

VL - 7443

BT - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Applications of Digital Image Processing XXXII

Y2 - 3 August 2009 through 5 August 2009

ER -

OCT image segmentation of the prostate nerves

Abstract

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ASJC Scopus subject areas

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