Classification of cross-section area of spinal canal on kernel-based support vector machine

Chao Cheng Wu; Hsiao Chi Li; Yung Hsiao Chiang; Jiannher Lin

doi:10.1109/ICSMC.2012.6378142

Classification of cross-section area of spinal canal on kernel-based support vector machine

Chao Cheng Wu, Hsiao Chi Li, Yung Hsiao Chiang, Jiannher Lin

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

4 Citations (Scopus)

Abstract

The cross section area of spinal canal has been an important indicator for lumbar spinal stenosis (LSS), which remains the leading preoperative diagnosis for adults older than 65 years. Due to its irregularity in spatial shape and lack of spectral information, this region can only be defined by doctors manually and calculated the amount of area by commercial software at present. The solution for reliable and robust classification and measurement remains open. This manuscript utilized kernel-based support vector machine to provide an automatically classification and measurement of the cross-section area of spinal canal. This kernel-based SVM classifier is compared with the linear SVM proposed in [1] and the present method. The experiments showed that the kernel based-SVM classifier could provide a better performance and robust classification result for the cross section area of spinal canal.

Original language	English
Title of host publication	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages	2622-2625
Number of pages	4
DOIs	https://doi.org/10.1109/ICSMC.2012.6378142
Publication status	Published - 2012
Event	2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of Duration: Oct 14 2012 → Oct 17 2012

Other

Other	2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Country/Territory	Korea, Republic of
City	Seoul
Period	10/14/12 → 10/17/12

Keywords

Classification
Kernel function
Radial basis function (RBF)
Spinal Canal
Support Vector Machine (SVM)

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

Access to Document

10.1109/ICSMC.2012.6378142

Cite this

Wu, CC, Li, HC, Chiang, YH & Lin, J 2012, Classification of cross-section area of spinal canal on kernel-based support vector machine. in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics., 6378142, pp. 2622-2625, 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012, Seoul, Korea, Republic of, 10/14/12. https://doi.org/10.1109/ICSMC.2012.6378142

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title = "Classification of cross-section area of spinal canal on kernel-based support vector machine",

abstract = "The cross section area of spinal canal has been an important indicator for lumbar spinal stenosis (LSS), which remains the leading preoperative diagnosis for adults older than 65 years. Due to its irregularity in spatial shape and lack of spectral information, this region can only be defined by doctors manually and calculated the amount of area by commercial software at present. The solution for reliable and robust classification and measurement remains open. This manuscript utilized kernel-based support vector machine to provide an automatically classification and measurement of the cross-section area of spinal canal. This kernel-based SVM classifier is compared with the linear SVM proposed in [1] and the present method. The experiments showed that the kernel based-SVM classifier could provide a better performance and robust classification result for the cross section area of spinal canal.",

keywords = "Classification, Kernel function, Radial basis function (RBF), Spinal Canal, Support Vector Machine (SVM)",

author = "Wu, {Chao Cheng} and Li, {Hsiao Chi} and Chiang, {Yung Hsiao} and Jiannher Lin",

year = "2012",

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T1 - Classification of cross-section area of spinal canal on kernel-based support vector machine

AU - Wu, Chao Cheng

AU - Li, Hsiao Chi

AU - Chiang, Yung Hsiao

AU - Lin, Jiannher

PY - 2012

Y1 - 2012

N2 - The cross section area of spinal canal has been an important indicator for lumbar spinal stenosis (LSS), which remains the leading preoperative diagnosis for adults older than 65 years. Due to its irregularity in spatial shape and lack of spectral information, this region can only be defined by doctors manually and calculated the amount of area by commercial software at present. The solution for reliable and robust classification and measurement remains open. This manuscript utilized kernel-based support vector machine to provide an automatically classification and measurement of the cross-section area of spinal canal. This kernel-based SVM classifier is compared with the linear SVM proposed in [1] and the present method. The experiments showed that the kernel based-SVM classifier could provide a better performance and robust classification result for the cross section area of spinal canal.

AB - The cross section area of spinal canal has been an important indicator for lumbar spinal stenosis (LSS), which remains the leading preoperative diagnosis for adults older than 65 years. Due to its irregularity in spatial shape and lack of spectral information, this region can only be defined by doctors manually and calculated the amount of area by commercial software at present. The solution for reliable and robust classification and measurement remains open. This manuscript utilized kernel-based support vector machine to provide an automatically classification and measurement of the cross-section area of spinal canal. This kernel-based SVM classifier is compared with the linear SVM proposed in [1] and the present method. The experiments showed that the kernel based-SVM classifier could provide a better performance and robust classification result for the cross section area of spinal canal.

KW - Classification

KW - Kernel function

KW - Radial basis function (RBF)

KW - Spinal Canal

KW - Support Vector Machine (SVM)

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BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

T2 - 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012

Y2 - 14 October 2012 through 17 October 2012

ER -

Classification of cross-section area of spinal canal on kernel-based support vector machine

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Keywords

ASJC Scopus subject areas

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