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

页码：299 / 308

页数：10

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