Hyperspectral Characterization of Atherosclerotic Plaques

Imaging modalities like hyperspectral imaging create large amounts of data. Time efficient, automated analytic techniques are therefore required to enjoy the power of such methods. In this study it was investigated if hyperspectral imaging followed by automated noise filtering and statistical image analysis is a suitable method for characterization of the macroscopic structure of atherosclerotic lesions. Ten human aorta samples (6x8 cm) were collected during autopsy. Hyperspectral white light and fluorescence images and 5 - 6 biopsies were collected from each sample. The biopsies were stained (HES, Sudan red), and grouped according to histology. All images were noise filtered and normalized. Fluorescence spectra were collected from all biopsied regions, and used to compute average spectra for each histological group. Supervised classification was performed using Spectral angle mapping (SAM) with the average spectra as endmembers. K-means-and ISO-data clustering was used for unsupervised classification. The results show that noise filtering and normalization is essential for reliable classification. Supervised classification was in general found to perform better than unsupervised classification. However, the SAM results strongly depend on the variation in the spectra used to compute the average endmember spectra. The analysis show that fatty deposits, calcifications, connective tissue and hemoglobin can be identified. The lesions were found to have a complex structure where vulnerable regions could be found next to stabile regions within the same lesion. In conclusion hyperspectral imaging, automated filtering and -analysis was found to be a suitable tool to classify advanced atherosclerotic lesions.