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 proceedingConference 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 languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4305-4310
Number of pages6
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - Feb 6 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: Oct 9 2016Oct 12 2016

Other

Other2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period10/9/1610/12/16

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

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  • 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