A real-time spectral mapper as an emerging diagnostic technology in biomedical sciences.

Real time spectral imaging and mapping at video rates can have tremendous impact not only on diagnostic sciences but also on fundamental physiological problems. We report the first real-time spectral mapper based on the combination of snap-shot spectral imaging and spectral estimation algorithms. Performance evaluation revealed that six band imaging combined with the Wiener algorithm provided high estimation accuracy, with error levels lying within the experimental noise. High accuracy is accompanied with much faster, by 3 orders of magnitude, spectral mapping, as compared with scanning spectral systems. This new technology is intended to enable spectral mapping at nearly video rates in all kinds of dynamic bio-optical effects as well as in applications where the target-probe relative position is randomly and fast changing.

A novel endoscopic spectral imaging platform integrating k-means clustering for early and non-invasive diagnosis of endometrial pathology.

We report a multimodal endoscopic system capable of performing both color and fast multispectral imaging in the spectral range 400-1000 nm. The system is based on a computer controllable tunable light source, which can be coupled with all types of endoscopes. Performance evaluation showed about 60% flat transmittance in almost all the operating wavelengths, at about 13 nm bandwidth per tuning step. With this system adapted to a thin hysteroscope, we also report, for the first time, spectral analysis of the endometrium and unsupervised/objective clustering of the spectra. We have implemented a method combining the k-means algorithm with the silhouette criterion for estimating the number of the distinguishable spectral classes that may correspond to different medical conditions of the tissue. It was found that there are five-well defined clusters of spectra, while preliminary clinical data seem to correlate well with the tissue pathology.