• 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.

Bayesian framework inspired no-reference region-of-interest quality measure for brain MRI images

Osadebey, Michael; Pedersen, Marius; Arnold, Douglas; Wendel-Mitoraj, Katrina; Neuroimaging Initiative, The Alzheimer’s Disease
Journal article, Peer reviewed
Accepted version
Thumbnail
View/Open
REVISED+MANUSCRIPT.pdf (2.958Mb)
URI
http://hdl.handle.net/11250/2465271
Date
2017
Metadata
Show full item record
Collections
  • Institutt for datateknologi og informatikk [3870]
  • Publikasjoner fra CRIStin - NTNU [20842]
Original version
Journal of Medical Imaging. 2017, 4 (2), 025504-1-025504-16.   10.1117/1.JMI.4.2.025504
Abstract
We describe a postacquisition, attribute-based quality assessment method for brain magnetic resonance imaging (MRI) images. It is based on the application of Bayes theory to the relationship between entropy and image quality attributes. The entropy feature image of a slice is segmented into low- and high-entropy regions. For each entropy region, there are three separate observations of contrast, standard deviation, and sharpness quality attributes. A quality index for a quality attribute is the posterior probability of an entropy region given any corresponding region in a feature image where quality attribute is observed. Prior belief in each entropy region is determined from normalized total clique potential (TCP) energy of the slice. For TCP below the predefined threshold, the prior probability for a region is determined by deviation of its percentage composition in the slice from a standard normal distribution built from 250 MRI volume data provided by Alzheimer’s Disease Neuroimaging Initiative. For TCP above the threshold, the prior is computed using a mathematical model that describes the TCP–noise level relationship in brain MRI images. Our proposed method assesses the image quality of each entropy region and the global image. Experimental results demonstrate good correlation with subjective opinions of radiologists for different types and levels of quality distortions.
Publisher
Society of Photo-optical Instrumentation Engineers (SPIE)
Journal
Journal of Medical 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