We address the automatic differentiation of human tissue using multispectral imaging with promising potential for automatic visualization during surgery. Currently, tissue types have to be continuously differentiated based on the surgeon's knowledge only. Further, automatic methods based on optical in vivo properties of human tissue do not yet exist, as these properties have not been sufficiently examined. To overcome this, we developed a hyperspectral camera setup to monitor the different optical behavior of tissue types in vivo. The aim of this work is to collect and analyze these behaviors to open up optical opportunities during surgery. Our setup uses a digital camera and several bandpass filters in front of the light source to illuminate different tissue types with 16 specific wavelength ranges. We analyzed the different intensities of eight healthy tissue types over the visible spectrum (400 to 700 nm). Using our setup and sophisticated postprocessing in order to handle motion during capturing, we are able to find tissue characteristics not visible for the human eye to differentiate tissue types in the 16-dimensional wavelength domain. Our analysis shows that this approach has the potential to support the surgeon's decisions during treatment.
Keywords: hyperspectral; intraoperative; monochromatic illumination; tissue classification; tissue differentiation.
(2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).