Abstract
The effective and non-invasive diagnosis of skin cancer is a hot topic in biophotonics since the current gold standard, biopsy followed by histological examination, is a slow and costly procedure for the healthcare system. Therefore, authors have put their efforts in characterizing skin cancer quantitatively through optical and photonic techniques such as 3D topography and multispectral imaging. Skin relief is an important biophysical feature that can be difficult to appreciate by touch, but can be precisely characterized with 3D imaging techniques, such as fringe projection. Color and spectral features given by skin chromophores, which are routinely analyzed by the naked eye and through dermoscopy, can also be quantified by means of multispectral imaging systems. In this study, the outcomes of these two imaging modalities were combined in a machine learning process to enhance classification of melanomas and nevi obtained from the two systems when operating isolately. The results suggest that the combination of 3D and multispectral data is relevant for the medical diagnosis of skin cancer.
© 2019 SPIE/OSA
PDF ArticleMore Like This
Di Wu, Anatoly Fedorov Kukk, Steffen Emmert, and Bernhard Roth
1262704 European Conference on Biomedical Optics (ECBO) 2023
Zahra Turani, Emad Fatemizadeh, Tatiana Blumetti, Steven Daveluy, Ana Flavia Moraes, Wei Chen, Darius Mehregan, Peter E. Andersen, and Mohammadreza Nasiriavanaki
11078_23 European Conference on Biomedical Optics (ECBO) 2019
A. Günther, C. Basu, B. Roth, and M. Meinhardt-Wollweber
87980O European Conference on Biomedical Optics (ECBO) 2013