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

DSpace/Manakin Repository

Show simple item record

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 http://hdl.handle.net/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.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

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record