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Journal of Biomedical Optics • Open Access

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy
Author(s): Jihye Seo; Yuri An; Jungsul Lee; Taeyun Ku; Yujung Kang; Chul Woo Ahn; Chulhee Choi

Paper 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.

Paper Details

Date Published: 12 April 2016
PDF: 9 pages
J. Biomed. Opt. 21(4) 046003 doi: 10.1117/1.JBO.21.4.046003
Published in: Journal of Biomedical Optics Volume 21, Issue 4
Show Author Affiliations
Jihye Seo, KAIST (Korea, Republic of)
Yuri An, KAIST (Korea, Republic of)
Jungsul Lee, KAIST (Korea, Republic of)
Taeyun Ku, KAIST (Korea)
Yujung Kang, Vieworks Co., Ltd (Korea)
Chul Woo Ahn, Yonsei Univ. College of Medicine (Korea)
Chulhee Choi, KAIST (Korea)
Korea Advanced Institute of Science and Technology (Korea)

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