Comparison of classification algorithms based on fluorescence data for the diagnosis of atherosclerosis

Yova, Dido; Gonis, Helen; Loukas, Spyros; Kassis Kyriakos, A; Koukoutsis, Elias; Papaodysseus Constantinos, N

dc.contributor.author	Yova, Dido	en
dc.contributor.author	Gonis, Helen	en
dc.contributor.author	Loukas, Spyros	en
dc.contributor.author	Kassis Kyriakos, A	en
dc.contributor.author	Koukoutsis, Elias	en
dc.contributor.author	Papaodysseus Constantinos, N	en
dc.date.accessioned	2014-03-01T02:48:24Z
dc.date.available	2014-03-01T02:48:24Z
dc.date.issued	1996	en
dc.identifier.issn	0277786X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33791
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0029669429&partnerID=40&md5=e097d1f62c96a74234f3bf68e501e34f	en
dc.subject.other	Atherosclerosis	en
dc.subject.other	Classification algorithms	en
dc.subject.other	Fluorescence spectroscopy	en
dc.subject.other	Kolmogorov-Smirnov fit	en
dc.subject.other	Tissue fluoriphores	en
dc.subject.other	Algorithms	en
dc.subject.other	Blood vessels	en
dc.subject.other	Diagnosis	en
dc.subject.other	Emission spectroscopy	en
dc.subject.other	Fluorescence	en
dc.subject.other	Statistical methods	en
dc.subject.other	Cardiology	en
dc.subject.other	Phosphors	en
dc.subject.other	Tissue	en
dc.subject.other	Medical imaging	en
dc.title	Comparison of classification algorithms based on fluorescence data for the diagnosis of atherosclerosis	en
heal.type	conferenceItem	en
heal.publicationDate	1996	en
heal.abstract	Fluorescence spectroscopy has been reported as a very promising approach for the discrimination between healthy and atherosclerotic arteries, as far as both, the spectral shape and the intensity of the corresponding spectra seem to be useful parameters for the diagnosis, at specific wavelengths. Nevertheless there are some difficulties in the precise diagnosis, mainly between the different categories of atherosclerotic arteries (fibrous, calcified, heavy calcified). These difficulties are based on the one hand on biophysical factors, such as the necessity for the preknowledge of tissue fluorophores or the complexity of tissue optics. On the other hand, different spectral classification algorithms have been used, such as multivariate linear regression, decision plane analysis and Bayesian decision analysis, each one with certain disadvantages. In this work, two different classification algorithms were developed and evaluated. During the first procedure, simple dimensionless functions were formed by the ratio of the intensities at selected wavelengths and the logistic model was used for statistical analysis. Decision surfaces were drawn and it was estimated that the probability of correct classification is 88%. The algorithm correctly discriminates 97% of healthy from diseased samples and 80% of fibrous from calcified coronary arteries. During the second procedure, a proper ratio was selected in the sense that the ratio groups of the populations P1 and P2 might be separated with an essential considerable veracity probability. The separability was confirmed by testing the validity of specific statistical hypotheses. The demonstration has been made by means of the Kolmogorov-Smirnov goodness of fit method. Therefore by applying statistical methods on proper parameters obtained from the specimens spectra, it has been able to automatically classify the arterial specimens into healthy (normal), fibrous, calcified and heavily calcified, with more than 99.9% probability (less than 0.1% confidence interval). The different classifications algorithms are thoroughly discussed and evaluated.	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.volume	2623	en
dc.identifier.spage	436	en
dc.identifier.epage	447	en