Hyperspectral images can provide useful biochemical information about tissue samples. Often, Fourier transform infrared (FTIR) images have been used to distinguish different tissue elements and changes caused by pathological causes. The spectral variation between tissue types and pathological states is very small and multivariate analysis methods are required to describe adequately these subtle changes. In this work, a strategy combining multivariate curve resolution-alternating least squares (MCR-ALS), a resolution (unmixing) method, which recovers distribution maps and pure spectra of image constituents, and K-means clustering, a segmentation method, which identifies groups of similar pixels in an image, is used to provide efficient information on tissue samples. First, multiset MCR-ALS analysis is performed on the set of images related to a particular pathology status to provide basic spectral signatures and distribution maps of the biological contributions needed to describe the tissues. Later on, multiset segmentation analysis is applied to the obtained MCR scores (concentration profiles), used as compressed initial information for segmentation purposes. The multiset idea is transferred to perform image segmentation of different tissue samples. Doing so, a difference can be made between clusters associated with relevant biological parts common to all images, linked to general trends of the type of samples analyzed, and sample-specific clusters, that reflect the natural biological sample-to-sample variability. The last step consists of performing separate multiset MCR-ALS analyses on the pixels of each of the relevant segmentation clusters for the pathology studied to obtain a finer description of the related tissue parts. The potential of the strategy combining multiset resolution on complete images, multiset segmentation and multiset local resolution analysis will be shown on a study focused on FTIR images of tissue sections recorded on inflamed and non-inflamed palatine tonsils.

Intravascular imaging techniques provide detailed specification about plaque appearance and morphology, but cannot deliver information about the biochemical composition of atherosclerotic plaques. As the biochemical composition is related to the plaque type, important aspects such as the risk of a plaque rupture and treatment are still difficult to assess. Currently, various spectroscopic techniques are tested for potential applications for the chemical analysis of plaque depositions. Here, we employ Raman spectroscopy in combination with optical coherence tomography (OCT) for the characterization of plaques on rabbits in vivo. Experiments were carried out on New Zealand white rabbits treated with a fat- and cholesterol-enriched diet, using a Raman probe setup with a 785-nm multimode laser as an excitation source. Subsequently, OCT images were acquired with a swept source at 1305±55  nm at 22.6 mW. Raman spectra were recorded from normal regions and regions with early plaque formations. The probe positioning was monitored by x-ray angiography. The spectral information identified plaque depositions consisting of lipids, with triglycerides as the major component. Afterward, OCT images of the spectroscopically investigated areas were obtained. The spectral information correlates well with the observed intravascular morphology and is in good agreement with histology. Raman spectroscopy can provide detailed biochemical specification of atherosclerotic plaques.

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