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)|
|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