Abstract
The WHO estimates three million new cases of skin cancer each year. Therefore, there exists a need for prescreening tools that can estimate the biological parameters of human skin, as they can help detect cancers before metastasis. In this paper, we present a novel inverse modeling technique based on Kubelka-Munk theory and machine learning to estimate biological skin parameters from in vivo hyperspec-tral imaging. We use the k-nearest neighbors (k-NN) algorithm in order to estimate skin parameters from their hy-perspectral signatures. We test our methods on 241 hyper-spectral signatures obtained from both genders and three ethnicities, and find encouraging results.
Original language | English (US) |
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Article number | 6627860 |
Pages (from-to) | 524-525 |
Number of pages | 2 |
Journal | Proceedings - IEEE Symposium on Computer-Based Medical Systems |
State | Published - Jan 1 2013 |
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Computer Science Applications