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12 April 2016 Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy
Jihye Seo, Yuri An, Jungsul Lee, Taeyun Ku M.D., Yujung Kang, Chul Woo Ahn, Chulhee Choi
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Journal of Biomedical Optics, Vol. 21, Issue 4, 046003 (April 2016). https://doi.org/10.1117/1.JBO.21.4.046003
Abstract
Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Jihye Seo, Yuri An, Jungsul Lee, Taeyun Ku M.D., Yujung Kang, Chul Woo Ahn, and Chulhee Choi "Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy," Journal of Biomedical Optics 21(4), 046003 (12 April 2016). https://doi.org/10.1117/1.JBO.21.4.046003
Published: 12 April 2016
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Cited by 15 scholarly publications and 2 patents.
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KEYWORDS
Luminescence
Control systems
Principal component analysis
Visualization
Diagnostics
Veins
Imaging systems
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