@inproceedings{5c2a4238a1294f61a670565059b9b215,
title = "Unsupervised Classification of Cerebrospinal Fluid by Statistical Indicators",
abstract = "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.",
keywords = "Cerebro spinal Fluid, Lumbar spinal stenosis, Magnetic resonance image, Measure of skewness, Segmentation, Thresholding, Unsupervised",
author = "Lee, {Kuan Ru} and Yeh, {Yi Xian} and Wu, {Chao Cheng} and Jiannher Lin and Chiang, {Yung Hsiao}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2019",
month = jan,
day = "16",
doi = "10.1109/SMC.2018.00648",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3827--3832",
booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
address = "United States",
}
