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dc.creatorTasoulis, S. K.en
dc.creatorMaglogiannis, I.en
dc.creatorPlagianakos, V. P.en
dc.date.accessioned2015-11-23T10:49:34Z
dc.date.available2015-11-23T10:49:34Z
dc.date.issued2012
dc.identifier10.1007/978-3-642-33409-2_40
dc.identifier.isbn9783642334085
dc.identifier.issn18684238
dc.identifier.urihttp://hdl.handle.net/11615/33576
dc.description.abstractThe advances in improved fluorescent probes and better cameras in collaboration with the advent of computers in imaging and image analysis, assist the task of diagnosis in many fields of biologic and medical research. In this paper, we introduce a computer-assisted image characterization tool based on a Fuzzy clustering method for the quantification of degree of Idiopathic Pulmonary Fibrosis (IPF) in medical images. The implementation of this algorithmic strategy is very promising concerning the issue of the automated assessment of microscopic images of lung fibrotic regions. © 2012 IFIP International Federation for Information Processing.en
dc.source.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870811544&partnerID=40&md5=b14a785ee1e2b1a9e0eec6a0ee5dec26
dc.subjectCluster Analysisen
dc.subjectFractal Dimensionen
dc.subjectFuzzy Clusteringen
dc.subjectImage Analysisen
dc.subjectAutomated assessmenten
dc.subjectComputer assisteden
dc.subjectFluorescent probesen
dc.subjectFuzzy C means clusteringen
dc.subjectFuzzy clustering methoden
dc.subjectIdiopathic pulmonary fibrosisen
dc.subjectMedical imagesen
dc.subjectMedical researchen
dc.subjectMicroscopic imageen
dc.subjectMicroscopy imagesen
dc.subjectUnsupervised detectionen
dc.subjectArtificial intelligenceen
dc.subjectDiagnosisen
dc.subjectFuzzy systemsen
dc.subjectMedical imagingen
dc.titleUnsupervised detection of fibrosis in microscopy images using fractals and fuzzy c-means clusteringen
dc.typeotheren


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