An automated system for optical imaging to characterize tissue based on diffuse reflectance spectroscopy

More interest is shown towards optical technologies that have the capability of performing in situ tissue diagnosis without the need for tissue biopsy. The ability to non-invasively detect early disease is a boon to the patients as well as the medical professionals. In actuality, detection at the fully formed but not spreading stage is acceptable. We must see lesions as they are transforming from a pre-cancerous state to a cancerous state, to be successful at very early detection. Early diagnosis is of paramount importance to manage precancerous and malignant lesions. Discrimination between normal-tissue and new tissue formation relies on morphological and functional pattern recognition. The functional state of tissue can influence its optical properties. Functional optical imaging capitalizes on the changing optical properties of tissue by using light to measure physiological changes. The basis for this imaging method arise from the differences in the spectra obtained from the normal and diseased tissue owing to the multiple physiological changes associated with increased vasculature, cellularity, oxygen consumption and edema in tumour. An optical fiber spectrometer is set up for this purpose, which is safe, portable and very affordable relative to other imaging modalities. The method involves exposure of skin surface to white light produced by an incandescent source. The back scattered photons emerging from various layers of tissue are detected by spectrometer resulting in tissue surface emission profile. Initially, the study is conducted on tissue equivalent phantoms The spectral data from the scan is processed using a suitable algorithm to form an image which can directly assist us in differentiating the part which is affected by disease visually. The unique information obtained from the diffuse optical spectroscopy makes it suitable for a variety of clinical applications, such as therapeutic monitoring, lesion characterization and risk assessment.