Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images

Maglogiannis, I; Sarimveis, H; Kiranoudis, CT; Chatziioannou, AA; Oikonomou, N; Aidinis, V

dc.contributor.author	Maglogiannis, I	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Kiranoudis, CT	en
dc.contributor.author	Chatziioannou, AA	en
dc.contributor.author	Oikonomou, N	en
dc.contributor.author	Aidinis, V	en
dc.date.accessioned	2014-03-01T01:29:04Z
dc.date.available	2014-03-01T01:29:04Z
dc.date.issued	2008	en
dc.identifier.issn	1089-7771	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19111
dc.subject	Image analysis	en
dc.subject	Interstitial pulmonary fibrosis	en
dc.subject	Neural networks	en
dc.subject	Quantitative assessment of microscopic images	en
dc.subject	Quantitative phenotypic classification	en
dc.subject	Support vector machines	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Mathematical & Computational Biology	en
dc.subject.classification	Medical Informatics	en
dc.subject.other	Algorithms	en
dc.subject.other	Feature extraction	en
dc.subject.other	Image analysis	en
dc.subject.other	Image recognition	en
dc.subject.other	Neural networks	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Support vector machines	en
dc.subject.other	Idiopathic pulmonary fibrosis	en
dc.subject.other	Interstitial pulmonary fibrosis	en
dc.subject.other	Microscopic images	en
dc.subject.other	Quantitative phenotypic classification	en
dc.subject.other	Medical imaging	en
dc.title	Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TITB.2006.888702	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TITB.2006.888702	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of biological microscopic images displaying lung tissue sections with idiopathic pulmonary fibrosis. For the development of the RBF classifiers, the fuzzy means clustering algorithm is utilized. This method is based on a fuzzy partition of the input space and requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied in lung sections acquired using a microscope and captured by a digital camera, at a magnification of 4×. Age- and sex-matched, 6- to 8-week-old mice (five for each time point and five as control) were used for the induction of pulmonary fibrosis (cf. bleomycin). Bleomycin administration initially induces lung inflammation that is followed by a progressive destruction of the normal lung architecture. The captured images correspond to 7, 15, and 23 days after bleomycin or saline injection and bronchoalveolar lavage (BAL) has been performed to the mice sample. The images were analyzed and color features were extracted. A support vector machines (SVMs)-based classifier was also employed for the same problem. The resulting scores derived by visual assessment of the images by expert pathologists were compared with the RBF and SVM classification outcome. Overall, the RBF neural network had a slightly better performance than that of the SVM classifier, but both performed very well, matching to a great percentage the scoring of the experts. There are some erroneous predictions of the algorithm for the regions characterized as ""ill""regions (i.e., some bronchia were wrongly classified as fibrotic areas); however, in general, the algorithm worked pretty fine in distinguishing pathologic from normal in most cases and for heterogeneous fibrotic foci, achieving high values in terms of specificity and sensitivity. © 2008 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Information Technology in Biomedicine	en
dc.identifier.doi	10.1109/TITB.2006.888702	en
dc.identifier.isi	ISI:000252517900007	en
dc.identifier.volume	12	en
dc.identifier.issue	1	en
dc.identifier.spage	42	en
dc.identifier.epage	54	en