Show simple item record

dc.contributor.authorTesfamicael, Solomon Abedom
dc.contributor.authorBarzideh, Faraz
dc.identifier.citationInternational Journal of Information and Electronics Engineering 2015, 5(1):46-50nb_NO
dc.description.abstracthis paper provides clustered compressive sensing (CCS) based image processing using Bayesian framework applied to medical images. Some images, for example like magnetic resonance images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. Compressed sensing (CS) paradigm can be applied in order to boost such signals. We applied CS paradigm via Bayesian framework. Using different sparse prior information and in addition incorporating the special structure that can be found in sparse signal, CCS can be applied to improve image processing. This is shown in the results of this paper. First, we applied our analysis on Angiogram image, then on Shepp-logan phantom and finally on another MRI image. The results show that applying the clustered compressive sensing give better results than the non-clustered version.nb_NO
dc.subjectBayesian framework, sparse prior, clustered prior, posterior, compressive sensing, LASSO, clustered LASSO.nb_NO
dc.titleClustered Compressive Sensing: Application on Medical Imagingnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.localcodeCC BY-NC-ND 4.0.nb_NO

Files in this item


This item appears in the following Collection(s)

Show simple item record
Except where otherwise noted, this item's license is described as