• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Øvrige samlinger
  • Publikasjoner fra CRIStin - NTNU
  • View Item
  •   Home
  • Øvrige samlinger
  • Publikasjoner fra CRIStin - NTNU
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images

Osadebey, Michael; Pedersen, Marius; Arnold, Douglas; Wendel-Mitoraj, Katrina
Journal article, Peer reviewed
Published version
Thumbnail
View/Open
Osadebey.pdf (1.740Mb)
URI
http://hdl.handle.net/11250/2617497
Date
2019
Metadata
Show full item record
Collections
  • Institutt for datateknologi og informatikk [3779]
  • Publikasjoner fra CRIStin - NTNU [19793]
Original version
Journal of Imaging. 2019, 5 (1), 1-23.   10.3390/jimaging5010020
Abstract
Noise-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.
Publisher
MDPI
Journal
Journal of Imaging

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit