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dc.contributor.authorOsadebey, Michael
dc.contributor.authorPedersen, Marius
dc.contributor.authorArnold, Douglas
dc.contributor.authorWendel-Mitoraj, Katrina
dc.date.accessioned2019-09-19T05:48:28Z
dc.date.available2019-09-19T05:48:28Z
dc.date.created2019-06-06T13:39:04Z
dc.date.issued2019
dc.identifier.citationJournal of Imaging. 2019, 5 (1), 1-23.nb_NO
dc.identifier.issn2313-433X
dc.identifier.urihttp://hdl.handle.net/11250/2617497
dc.description.abstractNoise-based quality evaluation of MRI images is highly desired in noise-dominant environments. Current noise-based MRI quality evaluation methods have drawbacks which limit their effective performance. Traditional full-reference methods such as SNR and most of the model-based techniques cannot provide perceptual quality metrics required for accurate diagnosis, treatment and monitoring of diseases. Although techniques based on the Moran coefficients are perceptual quality metrics, they are full-reference methods and will be ineffective in applications where the reference image is not available. Furthermore, the predicted quality scores are difficult to interpret because their quality indices are not standardized. In this paper, we propose a new no-reference perceptual quality evaluation method for grayscale images such as MRI images. Our approach is formulated to mimic how humans perceive an image. It transforms noise level into a standardized perceptual quality score. Global Moran statistics is combined with local indicators of spatial autocorrelation in the form of local Moran statistics. Quality score is predicted from perceptually weighted combination of clustered and random pixels. Performance evaluation, comparative performance evaluation and validation by human observers, shows that the proposed method will be a useful tool in the evaluation of retrospectively acquired MRI images and the evaluation of noise reduction algorithms.nb_NO
dc.language.isoengnb_NO
dc.publisherMDPInb_NO
dc.titleLocal Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Imagesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber1-23nb_NO
dc.source.volume5nb_NO
dc.source.journalJournal of Imagingnb_NO
dc.source.issue1nb_NO
dc.identifier.doi10.3390/jimaging5010020
dc.identifier.cristin1703182
dc.relation.projectNorges forskningsråd: 247689nb_NO
dc.description.localcode© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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