For obtaining functional parameters, we previously proposed an optical imaging method, dynamic fluorescence imaging (DyFI), based on the time-series analysis of indocyanine green (ICG) pharmacodynamics.4 ICG, an FDA-approved, nonspecific near-infrared fluorophore, has been widely used for detection of synovitis,5,6 sentinel lymph node,7 rheumatoid arthritis,8–10 breast cancer imaging,11 and studies for vascular events.12,13 With DyFI, we can measure perfusion rate with higher accuracy and sensitivity compared to other conventional methods. Previous studies have shown that analysis of time-series ICG images can predict the prognosis of murine hindlimb ischemia.4 Additionally, by analyzing ICG serial images of dorsal feet, we were able to identify clinical features associated with peripheral vascular insufficiency; for example, a reliable characteristic feature of Raynaud phenomenon (RP) was identified as modified , calculated as the length of time between peak onset and maximum peak fluorescence.14 Perfusion rate has also been used as a quantitative measure of tissue perfusion, with sufficient sensitivity to diagnose mild peripheral arterial occlusive disease.15 Recently, symmetricity analysis of the left and right extremities has been used to diagnose microvascular abnormalities in feet.16 Our ICG fluorescence images have and 120 time frames. The large amount of data contained in these images may cause dimensionality issues that can severely restrict its practical application.17 The use of raw high-dimensional data makes it difficult to extract the important elements that form the representative pattern of ICG fluorescence dynamics. Previously suggested features, such as the modified 14 and the perfusion rate,15 are susceptible to noise signals and movement artifacts. Furthermore, in cases of increased vascular permeability, functional parameters may be underestimated. For these reasons, the present study was conducted to assess the entire set of spatiotemporal data using only a few components, rather than thousands of variables.