Evaluation of band generation process for classification of cerebrospinal fluid in magnetic resonance images

Kuan Ru Lee; Chao Cheng Wu; Yung Hsiao Chiang; Jiannher Lin

doi:10.1109/SMC.2016.7844908

Evaluation of band generation process for classification of cerebrospinal fluid in magnetic resonance images

Kuan Ru Lee, Chao Cheng Wu, Yung Hsiao Chiang, Jiannher Lin

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

1 Citation (Scopus)

Abstract

Cross section area (CSA) 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. Until recently, the machine learning algorithms had been investigated in [5-7] for an automatic classification system. The automatic classification system exploited the luminance of cerebrospinal fluid (CSF) as the major features. Unfortunately, the limited sequences of magnetic resonance images, which included only T₁ and T₂ sequences, produced certain level of false alarm and reduced the classification rate. The band expansion process(BEP) proposed in [8] shed light on this issue by generating additional bands with non-linear functions. The idea of BEP unveils the non-linear relationship among sequences to increase the classification rate. The utilities of BEP had been evaluated in brain MR images [9]. This paper would like to extend the applications of BEP for classification of CSF. The experimental studies further demonstrated the benefits of the BEP.

Original language	English
Title of host publication	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4305-4310
Number of pages	6
ISBN (Electronic)	9781509018970
DOIs	https://doi.org/10.1109/SMC.2016.7844908
Publication status	Published - Feb 6 2017
Event	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary Duration: Oct 9 2016 → Oct 12 2016

Other

Other	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Country/Territory	Hungary
City	Budapest
Period	10/9/16 → 10/12/16

Keywords

Band expansion process
Cerebrospinal Fluid
Lumbar spinal stenosis
Support vector machine
Unsupervised Classification

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Artificial Intelligence
Control and Optimization
Human-Computer Interaction

Access to Document

10.1109/SMC.2016.7844908

Cite this

Lee, K. R., Wu, C. C., Chiang, Y. H., & Lin, J. (2017). Evaluation of band generation process for classification of cerebrospinal fluid in magnetic resonance images. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 4305-4310). [7844908] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2016.7844908

Evaluation of band generation process for classification of cerebrospinal fluid in magnetic resonance images. / Lee, Kuan Ru; Wu, Chao Cheng; Chiang, Yung Hsiao et al.
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4305-4310 7844908.

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

Lee, KR, Wu, CC, Chiang, YH & Lin, J 2017, Evaluation of band generation process for classification of cerebrospinal fluid in magnetic resonance images. in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings., 7844908, Institute of Electrical and Electronics Engineers Inc., pp. 4305-4310, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, 10/9/16. https://doi.org/10.1109/SMC.2016.7844908

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abstract = "Cross section area (CSA) 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. Until recently, the machine learning algorithms had been investigated in [5-7] for an automatic classification system. The automatic classification system exploited the luminance of cerebrospinal fluid (CSF) as the major features. Unfortunately, the limited sequences of magnetic resonance images, which included only T1 and T2 sequences, produced certain level of false alarm and reduced the classification rate. The band expansion process(BEP) proposed in [8] shed light on this issue by generating additional bands with non-linear functions. The idea of BEP unveils the non-linear relationship among sequences to increase the classification rate. The utilities of BEP had been evaluated in brain MR images [9]. This paper would like to extend the applications of BEP for classification of CSF. The experimental studies further demonstrated the benefits of the BEP.",

keywords = "Band expansion process, Cerebrospinal Fluid, Lumbar spinal stenosis, Support vector machine, Unsupervised Classification",

author = "Lee, {Kuan Ru} and Wu, {Chao Cheng} and Chiang, {Yung Hsiao} and Jiannher Lin",

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AB - Cross section area (CSA) 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. Until recently, the machine learning algorithms had been investigated in [5-7] for an automatic classification system. The automatic classification system exploited the luminance of cerebrospinal fluid (CSF) as the major features. Unfortunately, the limited sequences of magnetic resonance images, which included only T1 and T2 sequences, produced certain level of false alarm and reduced the classification rate. The band expansion process(BEP) proposed in [8] shed light on this issue by generating additional bands with non-linear functions. The idea of BEP unveils the non-linear relationship among sequences to increase the classification rate. The utilities of BEP had been evaluated in brain MR images [9]. This paper would like to extend the applications of BEP for classification of CSF. The experimental studies further demonstrated the benefits of the BEP.

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Evaluation of band generation process for classification of cerebrospinal fluid in magnetic resonance images

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