Combined texture features for improved classification of suspicious areas in autofluorescence bronchoscopy

Bountris, P; Apostolou, A; Haritou, M; Passalidou, E; Koutsouris, D

dc.contributor.author	Bountris, P	en
dc.contributor.author	Apostolou, A	en
dc.contributor.author	Haritou, M	en
dc.contributor.author	Passalidou, E	en
dc.contributor.author	Koutsouris, D	en
dc.date.accessioned	2014-03-01T02:46:03Z
dc.date.available	2014-03-01T02:46:03Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32513
dc.subject	Autofluorescence bronchoscopy (AFB)	en
dc.subject	Classification	en
dc.subject	Feature selection	en
dc.subject	Lung cancer	en
dc.subject	Texture	en
dc.subject.other	Autofluorescence	en
dc.subject.other	Classification features	en
dc.subject.other	Classification models	en
dc.subject.other	Clinical trial	en
dc.subject.other	Detection and localization	en
dc.subject.other	Diagnostic value	en
dc.subject.other	False positive	en
dc.subject.other	Feature selection	en
dc.subject.other	Feature selection methods	en
dc.subject.other	High rate	en
dc.subject.other	Lung Cancer	en
dc.subject.other	Malignant lesion	en
dc.subject.other	Texture features	en
dc.subject.other	Biological organs	en
dc.subject.other	Endoscopy	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Information technology	en
dc.subject.other	Intelligent computing	en
dc.subject.other	Textures	en
dc.title	Combined texture features for improved classification of suspicious areas in autofluorescence bronchoscopy	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ITAB.2009.5394448	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ITAB.2009.5394448	en
heal.identifier.secondary	5394448	en
heal.publicationDate	2009	en
heal.abstract	Autofluorescence bronchoscopy (AFB) has been utilized over th e past decade, proving to be a powerful tool for the detection and localization of premalignant and malignant lesions of the airways. AFB is, however, characterized by low specificity and a high rate of false positive findings (FPFs). The majority of FPFs are due to inflammations, as they often fluoresce at the same wavelengths with cancer. According to several clinical trials, the percentage of the FPFs is a bout 30%. In this paper we present an intelligent computing system based on combined texture features, feature selection methods and classification models, for improved classification of suspicious areas of the bronchial mucosa, in order to decrease the rate of FPFs, to increase the specificity and sensitivity of AFB and enhance the overall diagnostic value of the AFB method. ©2009 IEEE.	en
heal.journalName	Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009	en
dc.identifier.doi	10.1109/ITAB.2009.5394448	en