The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion's biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

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We propose in this paper a fuzzy algorithm for modeling medical diagnostic processes. A new aggregation rule is employed to handle in a better way imprecision and relative importance of symptoms. An application example is presented to illustrate the proposed algorithm.

This article discusses the digital processing methodology utilized to analyze Raman spectral data with an ultimate aim to develop a rapid and automatic system for atherosclerosis diagnosis. Different types of digital and wavelet transform filters have been studied in order to reduce the CCD detector noise. After calibration, Raman spectrum has been processed by an automatic program that classifies the target tissue into pathologic or nonpathologic using pattern recognition techniques. To validate the diagnosis inferred by the automated system, a collection of 70 spectra from human coronary arteries has been tested and compared with the histological method. The processing time of whole analysis is as small as 10 milliseconds when the program is executed in a processing station based on the ADSP 61061 Sharc Digital Signal Processor.

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