Light refraction based medical image segmentation

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dc.contributor.authorGüvenç, Uğur
dc.contributor.authorDemirci, Recep
dc.contributor.authorKaragül, Tuba
dc.date.accessioned2020-04-30T23:18:56Z
dc.date.available2020-04-30T23:18:56Z
dc.date.issued2010
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionDemirci, Recep/0000-0002-3278-0078; guvenc, ugur/0000-0002-5193-7990en_US
dc.descriptionWOS: 000278594400011en_US
dc.description.abstractImage segmentation is the most important and first step in pattern recognition and image analysis. In this paper, an automatic segmentation algorithm based on light refraction was proposed for medical images. In the proposed algorithm, similarity percents of the pixels were calculated by using the amount of shift while occurred light through a transparent sheet and re-enters the same environment. The proposed algorithm is similar to region growing algorithm where the seed points are automatically selected and grown and does not require any prior knowledge of the number of regions existing in the image. So, it decreases the computational load required for the other image segmentation methods. The proposed algorithm is demonstrated by application to real medical images. Results have showed that proposed algorithm extract all segments effectively.en_US
dc.identifier.endpage1132en_US
dc.identifier.issn1992-2248
dc.identifier.issue10en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1127en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/3586
dc.identifier.volume5en_US
dc.identifier.wosWOS:000278594400011en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademic Journalsen_US
dc.relation.ispartofScientific Research And Essaysen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLight refraction lawen_US
dc.subjectimage segmentationen_US
dc.subjectmedical imagesen_US
dc.titleLight refraction based medical image segmentationen_US
dc.typeArticleen_US

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