Development and evaluation of pattern recognition techniques for fluorescence diagnosis of atherosclerosis

Papaodysseus Constantinos, N; Kassis Kyriakos, A; Gonis, Helen; Yova, Dido

dc.contributor.author	Papaodysseus Constantinos, N	en
dc.contributor.author	Kassis Kyriakos, A	en
dc.contributor.author	Gonis, Helen	en
dc.contributor.author	Yova, Dido	en
dc.date.accessioned	2014-03-01T02:48:24Z
dc.date.available	2014-03-01T02:48:24Z
dc.date.issued	1996	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33795
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0029768277&partnerID=40&md5=4779afa4b59b4d49aa1ea195b4fb210f	en
dc.subject.other	Chromophores	en
dc.subject.other	Coronary atherosclerosis	en
dc.subject.other	Fluorescence diagnosis	en
dc.subject.other	Algorithms	en
dc.subject.other	Cardiology	en
dc.subject.other	Diagnosis	en
dc.subject.other	Emission spectroscopy	en
dc.subject.other	Fluorescence	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Tissue	en
dc.subject.other	Medical imaging	en
dc.title	Development and evaluation of pattern recognition techniques for fluorescence diagnosis of atherosclerosis	en
heal.type	conferenceItem	en
heal.publicationDate	1996	en
heal.abstract	A field where fluorescence spectroscopy might be of great interest for diagnosis, is coronary atherosclerosis and therefore spectroscopic characterization of cardiovascular tissues has been extensively studied. Nevertheless there are several limitations in the precise interpretation of the spectroscopic differences, between normal and atherosclerotic arteries since the tissue is a complex and multilayer structure. Therefore the spectra of individual chromophores could overlap and re-absorption phenomena could occur, too. Another major difficulty arises from the necessity of convenient classification algorithms and the assessment of their feasibility to use fluorescence information, for accurate diagnosis. As a result in order to assess the feasibility of utilizing spectral information to discriminate arterial tissue type several classification algorithms were developed and evaluated. In this work the following pattern recognition techniques have been tested and evaluated: (1) Distance measure (or norm, or metric) based pattern recognition techniques. Methodologically speaking, based on the histopathological diagnosis, a training set of spectra has been classified into four different categories (healthy, fibrous, calcified, heavy calcified) and in each of these four training groups a representative spectrum has been recorded. (2) A pattern recognition method based on statistical considerations. Discrimination between either the four aforementioned classes (categories) or pairs of them is achieved since peak intensities in appropriate wavelengths appear to correlate efficiently with tissue type. The difference of each training set member from the corresponding representative has been defined by using various appropriate distance measures and the sample statistical properties for each category of the training group has been found. Appropriate statistical analysis has been performed in order to deduce the distribution of the distance measures and of the coefficients of the whole population for each one of the four categories, with at least 99% confidence interval. A validation set of samples has been used in order to test and compare the aforementioned pattern recognition algorithms. A performance comparison of the aforementioned algorithms has been undertaken.	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.volume	2671	en
dc.identifier.spage	116	en
dc.identifier.epage	124	en