Contrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance images

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dc.contributor.authorYilmaz, Burak
dc.contributor.authorOzbay, Yuksel
dc.date.accessioned2020-03-26T18:49:53Z
dc.date.available2020-03-26T18:49:53Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBrain magnetic resonance imaging (MRI) images support important information about brain diseases for physicians. Morphological alterations in brain tissues indicate the probable existence of a disease in many cases. Proper estimation of these tissues, measuring their sizes, and analyzing their image patterns are parts of the diagnosis process. Therefore, the interpretability and perceptibility level of the MRI image is valuable for physicians. In this paper, a new image contrast enhancement algorithm based on linear combinations is presented. The proposed algorithm is focused on improving the interpretability and perceptibility of the image information. An MRI image is presented to the algorithm, which generates a set of images from this MRI image. After this step, the algorithm uses the linear combination technique for combining the image set to generate a final image. Linear combination coefficients are generated using the artificial bee colony algorithm. The algorithm is evaluated by 4 different global image enhancement evaluation techniques: contrast improvement ratio (CIR), enhancement measurement error (EME), absolute mean brightness error (AMBE), and peak-signal-to-noise ratio (PSNR). During the evaluation process, 2 case studies are performed. The first case study is performed with 3 different image sets (T1, T2, and proton density) presented to the algorithm. These sets are obtained from the Brainweb simulated MRI database. The algorithm shows the best performance on the T1 image set with 5.844 CIR, 6.217 EME, 15.045 AMBE, and 22.150 dB PSNR scores as average values. The second case study is also performed with 3 different image sets (T1-fast low-angle shot sequence, T1-magnetization-prepared rapid acquired gradient-echoes (MP-RAGE), and T2) obtained from the The Multimedia Digital Archiving System public community database. The algorithm performs best with the T1-MP-RAGE modality images with 6.983 CIR, 17.326 EME, 3.514 AMBE, and 30.157 dB PSNR scores as average values. In addition, this algorithm can be used for classification tasks with proper linear combination coefficients, for instance, segmentation of the white matter regions in brain MRI images.en_US
dc.identifier.doi10.3906/elk-1209-31en_US
dc.identifier.endpage1563en_US
dc.identifier.issn1300-0632en_US
dc.identifier.issn1303-6203en_US
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1540en_US
dc.identifier.urihttps://dx.doi.org/10.3906/elk-1209-31
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30705
dc.identifier.volume22en_US
dc.identifier.wosWOS:000344740600012en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYen_US
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectImage contrast enhancementen_US
dc.subjectlinear combinationen_US
dc.subjectartificial bee colony algorithmen_US
dc.subjectimage processingen_US
dc.subjectMRIen_US
dc.subjectmultiple sclerosisen_US
dc.titleContrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance imagesen_US
dc.typeArticleen_US

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