Identifying different types of tumors in human brain, breast, and uterus according to their optical properties in the red to near-infrared region

Optical diagnosis techniques are gaining validity due to their competitive advantages including safety and functionality. These methods probe the tissue using light in the red to near-infrared region that shows relatively high penetration in biological tissues. The reflected light which is controlled by tissue absorption and scattering parameters can provide significant information about its pathology. In the present work, a method for identifying different types of tumors in human brain, breast and uterus based on their absorption and scattering characteristics in the red to near-infrared spectra is proposed. The classification method depends on studying the red to near-infrared light diffusion through the examined tissues using Monte-Carlo simulation and finite element solution of the light diffusion equation. The obtained tissue reflectance profiles, optical fluence rate distribution and absorbed faction in each tumor type show different characteristics along the selected wavelengths. The variation in these three features can be considered as an alternative approach of medical diagnosis or can assist with other conventional diagnosis methods.