Unsupervised Classification of Cerebrospinal Fluid by Statistical Indicators

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

研究成果: 書貢獻/報告類型會議貢獻

摘要

Cross section area (CSA) of spinal canal has been a crucial indicator for lumbar spinal stenosis (LSS), which remains the leading preoperative diagnosis for elder people. Recently, the machine learning algorithms have been investigated in[7-10] for automatic classification systems. The methods investigated in[7-10] exploited the characteristics of cerebrospinal fluid (CSF) in T1 and T2 sequences of MRI images. Nevertheless, in order to apply the trained classifiers, the differences among images need to be as small as possible due to the nature of classification. To address the issue, this paper reinvented the wheel to propose unsupervised segmentation method without requirement of training process. Based on the characteristic property of skewness, the proposed algorithm can also distinguish the finer details such as nerve roots from CSF. The experimental study further demonstrated the benefits of proposed framework.
原文英語
主出版物標題Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面3827-3832
頁數6
ISBN(電子)9781538666500
DOIs
出版狀態已發佈 - 一月 16 2019
事件2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, 日本
持續時間: 十月 7 2018十月 10 2018

出版系列

名字Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

會議

會議2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
國家/地區日本
城市Miyazaki
期間10/7/1810/10/18

ASJC Scopus subject areas

  • 資訊系統
  • 資訊系統與管理
  • 健康資訊學
  • 人工智慧
  • 電腦網路與通信
  • 人機介面

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