1 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
主出版物標題2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面4305-4310
頁數6
ISBN(電子)9781509018970
DOIs
出版狀態已發佈 - 二月 6 2017
事件2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, 匈牙利
持續時間: 十月 9 2016十月 12 2016

其他

其他2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
國家/地區匈牙利
城市Budapest
期間10/9/1610/12/16

ASJC Scopus subject areas

  • 電腦視覺和模式識別
  • 人工智慧
  • 控制和優化
  • 人機介面

指紋

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