Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en_US |
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SPIE
2016
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| Online Access: | http://hdl.handle.net/1721.1/103534 https://orcid.org/0000-0001-9447-7579 |
| _version_ | 1826187997672898560 |
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| author | Seo, Jihye An, Yuri Lee, Jungsul Ku, Taeyun Kang, Yujung Ahn, Chulwoo Choi, Chulhee |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Seo, Jihye An, Yuri Lee, Jungsul Ku, Taeyun Kang, Yujung Ahn, Chulwoo Choi, Chulhee |
| author_sort | Seo, Jihye |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T07:53:01Z |
| format | Article |
| id | mit-1721.1/103534 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:53:01Z |
| publishDate | 2016 |
| publisher | SPIE |
| record_format | dspace |
| spelling | mit-1721.1/1035342022-09-30T00:46:45Z Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy Seo, Jihye An, Yuri Lee, Jungsul Ku, Taeyun Kang, Yujung Ahn, Chulwoo Choi, Chulhee Institute for Medical Engineering and Science Ku, Taeyun 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. National Research Foundation of Korea (Bio & Medical Technology Development Program, Korean government funding, MSIP (No. 2011-0019697) 2016-07-07T15:17:07Z 2016-07-07T15:17:07Z 2016-04 2016-01 Article http://purl.org/eprint/type/JournalArticle 1083-3668 http://hdl.handle.net/1721.1/103534 Seo, Jihye, Yuri An, Jungsul Lee, Taeyun Ku, Yujung Kang, Chulwoo Ahn, and Chulhee Choi. “Principal Component Analysis of Dynamic Fluorescence Images for Diagnosis of Diabetic Vasculopathy.” Journal of Biomedical Optics 21, no. 4 (April 12, 2016): 046003. https://orcid.org/0000-0001-9447-7579 en_US http://dx.doi.org/10.1117/1.jbo.21.4.046003 Journal of Biomedical Optics Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf SPIE SPIE |
| spellingShingle | Seo, Jihye An, Yuri Lee, Jungsul Ku, Taeyun Kang, Yujung Ahn, Chulwoo Choi, Chulhee Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy |
| title | Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy |
| title_full | Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy |
| title_fullStr | Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy |
| title_full_unstemmed | Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy |
| title_short | Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy |
| title_sort | principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy |
| url | http://hdl.handle.net/1721.1/103534 https://orcid.org/0000-0001-9447-7579 |
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