Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia

被引：33
|
作者：
Mansfield, JR [1 ]
Sowa, MG [1 ]
Scarth, GB [1 ]
Somorjai, RL [1 ]
Mantsch, HH [1 ]
机构：
[1] Natl Res Council Canada, Inst Biodiagnost, Winnipeg, MB R3B 1Y6, Canada
来源：
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 1997年 / 21卷 / 05期
关键词：
near-infrared imaging; forearm ischemia; image analysis; PCA; fuzzy C-means clustering; SKELETAL-MUSCLE OXYGENATION; FUNCTIONAL CONNECTIVITY; NONINVASIVE MEASUREMENT; SPECTROSCOPY; CONSUMPTION; PET;
D O I：
10.1016/S0895-6111(97)00018-9
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Fuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm. (C) 1997 Elsevier Science Ltd.
引用
收藏
页码：299 / 308
页数：10
相关论文
共 50 条
  • [1] Tissue viability by multispectral near infrared imaging: A fuzzy C-means clustering analysis
    Mansfield, JR
    Sowa, MG
    Payette, JR
    Abdulrauf, B
    Stranc, MF
    Mantsch, HH
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (06) : 1011 - 1018
  • [2] Clustering by Unified Principal Component Analysis and Fuzzy C-Means with Sparsity Constraint
    Wang, Jikui
    Shi, Quanfu
    Yang, Zhengguo
    Nie, Feiping
    ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2020, PT II, 2020, 12453 : 337 - 351
  • [3] Optimized fuzzy C-means clustering algorithm for the interpretation of the near-infrared spectra of rocks
    Wu Fang
    Wei Liang-shu
    Huang Jun-jie
    Liu Gui-ling
    Jiang Xi-ping
    SPECTROSCOPY LETTERS, 2017, 50 (05) : 270 - 274
  • [4] Using principal component analysis and fuzzy c-means clustering for the assessment of air quality monitoring
    Dogruparmak, Senay Cetin
    Keskin, Gulsen Aydin
    Yaman, Selin
    Alkan, Atakan
    ATMOSPHERIC POLLUTION RESEARCH, 2014, 5 (04) : 656 - 663
  • [5] Robust Recommendation Algorithm Based on Kernel Principal Component Analysis and Fuzzy C-means Clustering
    YI Huawei
    NIU Zaiseng
    ZHANG Fuzhi
    LI Xiaohui
    WANG Yajun
    Wuhan University Journal of Natural Sciences, 2018, 23 (02) : 111 - 119
  • [6] Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering
    Rosen, C
    Yuan, Z
    WATER SCIENCE AND TECHNOLOGY, 2001, 43 (07) : 147 - 156
  • [7] UNSUPERVISED CHANGE DETECTION IN SATELLITE IMAGES USING FUZZY C-MEANS CLUSTERING AND PRINCIPAL COMPONENT ANALYSIS
    Kesikoglu, M. H.
    Atasever, U. H.
    Ozkan, C.
    ISPRS2013-SSG, 2013, 40-7-W2 : 129 - 132
  • [8] Classification of lignocellulosic biomass by weighted-covariance factor fuzzy C-means clustering of mid-infrared and near-infrared spectra
    Rammal, Abbas
    Perrin, Eric
    Vrabie, Valeriu
    Bertrand, Isabelle
    Chabbert, Brigitte
    JOURNAL OF CHEMOMETRICS, 2017, 31 (02)
  • [9] Optimization of Fuzzy C-Means Clustering Algorithm with Combination of Minkowski and Chebyshev Distance Using Principal Component Analysis
    Sugiyarto Surono
    Rizki Desia Arindra Putri
    International Journal of Fuzzy Systems, 2021, 23 : 139 - 144
  • [10] Optimization of Fuzzy C-Means Clustering Algorithm with Combination of Minkowski and Chebyshev Distance Using Principal Component Analysis
    Surono, Sugiyarto
    Putri, Rizki Desia Arindra
    INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2021, 23 (01) : 139 - 144
12345