28 February 2013 Hyperspectral signature analysis of skin parameters
Author Affiliations +
Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867002 (2013) https://doi.org/10.1117/12.2001428
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
The temporal analysis of changes in biological skin parameters, including melanosome concentration, collagen concentration and blood oxygenation, may serve as a valuable tool in diagnosing the progression of malignant skin cancers and in understanding the pathophysiology of cancerous tumors. Quantitative knowledge of these parameters can also be useful in applications such as wound assessment, and point-of-care diagnostics, amongst others. We propose an approach to estimate in vivo skin parameters using a forward computational model based on Kubelka-Munk theory and the Fresnel Equations. We use this model to map the skin parameters to their corresponding hyperspectral signature. We then use machine learning based regression to develop an inverse map from hyperspectral signatures to skin parameters. In particular, we employ support vector machine based regression to estimate the in vivo skin parameters given their corresponding hyperspectral signature. We build on our work from SPIE 2012, and validate our methodology on an in vivo dataset. This dataset consists of 241 signatures collected from in vivo hyperspectral imaging of patients of both genders and Caucasian, Asian and African American ethnicities. In addition, we also extend our methodology past the visible region and through the short-wave infrared region of the electromagnetic spectrum. We find promising results when comparing the estimated skin parameters to the ground truth, demonstrating good agreement with well-established physiological precepts. This methodology can have potential use in non-invasive skin anomaly detection and for developing minimally invasive pre-screening tools.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Saurabh Vyas, Amit Banerjee, Luis Garza, Sewon Kang, Philippe Burlina, "Hyperspectral signature analysis of skin parameters", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867002 (28 February 2013); doi: 10.1117/12.2001428; https://doi.org/10.1117/12.2001428
PROCEEDINGS
8 PAGES


SHARE
Back to Top