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Retinal Spectral Image Analysis Methods using Spectral Reflectance Pattern Recognition
Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences. (Digital Printing Center)ORCID iD: 0000-0001-7387-6650
School of Engineering, Monash University, Sunway Campus, Malaysia.
SIB Labs., School of Computing, University of Eastern Finland, Finland.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences. (Digital Printing Center)
2013 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin Heidelberg: Springer, 2013, Vol. 7786, p. 224-238Conference paper, Published paper (Refereed)
Abstract [en]

Conventional 3-channel color images have limited information andquality dependency on parametric conditions. Hence, spectral imaging andreproduction is desired in many color applications to record and reproduce thereflectance of objects. Likewise RGB images lack sufficient information tosuccessfully analyze diabetic retinopathy. In this case, spectral imaging may bethe alternative solution. In this article, we propose a new supervised techniqueto detect and classify the abnormal lesions in retinal spectral reflectance imagesaffected by diabetes. The technique employs both stochastic and deterministicspectral similarity measures to match the desired reflectance pattern. At first, itclassifies a pixel as normal or abnormal depending on the probabilistic behaviorof training spectra. The final decision is made evaluating the geometricsimilarity. We assessed several multispectral object detection methodsdeveloped for other applications. They could not proof to be the solution. Theresults were interpreted using receiver operating characteristics (ROC) curvesanalysis.

Place, publisher, year, edition, pages
Berlin Heidelberg: Springer, 2013. Vol. 7786, p. 224-238
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7786
Keywords [en]
Spectral reflectance image, Diabetic retinopathy, Spectral information divergence, ROC curves, Object detection, Objects classification
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:miun:diva-18481DOI: 10.1007/978-3-642-36700-7_18ISI: 000342983600018Scopus ID: 2-s2.0-84875107126ISBN: 978-364236699-4 (print)OAI: oai:DiVA.org:miun-18481DiVA, id: diva2:605635
Conference
4th Computational Color Imaging Workshop, CCIW 2013;Chiba;3 March 2013through5 March 2013;Code96014
Projects
EU Erasmus Mundus CIMETFinnish Funding Agency for Technology and Innovation (TEKES Project 40039/07)Available from: 2013-02-14 Created: 2013-02-14 Last updated: 2018-01-11Bibliographically approved

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Rahaman, G M AtiqurNorberg, Ole

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