1 July 2010 Principal component model of multispectral data for near real-time skin chromophore mapping
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Abstract
Multispectral images of skin contain information on the spatial distribution of biological chromophores, such as blood and melanin. From this, parameters such as blood volume and blood oxygenation can be retrieved using reconstruction algorithms. Most such approaches use some form of pixelwise or volumetric reconstruction code. We explore the use of principal component analysis (PCA) of multispectral images to access blood volume and blood oxygenation in near real time. We present data from healthy volunteers under arterial occlusion of the forearm, experiencing ischemia and reactive hyperemia. Using a two-layered analytical skin model, we show reconstruction results of blood volume and oxygenation and compare it to the results obtained from our new spectral analysis based on PCA. We demonstrate that PCA applied to multispectral images gives near equivalent results for skin chromophore mapping and quantification with the advantage of being three orders of magnitude faster than the reconstruction algorithm.
© (2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Jana M. Kainerstorfer, Martin Ehler, Franck Amyot, Moinuddin Hassan, Stavros G. Demos, Victor V. Chernomordik, Christoph K. Hitzenberger, Amir H. Gandjbakhche, Jason D. Riley, "Principal component model of multispectral data for near real-time skin chromophore mapping," Journal of Biomedical Optics 15(4), 046007 (1 July 2010). https://doi.org/10.1117/1.3463010 . Submission:
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