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10.1109/JBHI.2015.2439242 http://scihub22266oqcxt.onion/10.1109/JBHI.2015.2439242 C4562064!4562064!26340685     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2015 ; 19 (5): 1598-609 Nephropedia Template TP {{tp|p=26340685|t=2015. Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation |pdf=|usr=}}{{26340685}} gab.com Text Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=26340685 #FOAvip Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available, state-of-the art approaches. Twit Text FOAVip Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=26340685 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26340685 #FOAvip English Wikipedia <ref>{{Cite pmid|26340685}}</ref> Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation #MMPMID26340685 Roy S; He Q; Sweeney E; Carass A; Reich DS; Prince JL; Pham DL IEEE J Biomed Health Inform 2015[Sep]; 19 (5): 1598-609 PMID26340685show ga Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available, state-of-the art approaches. äSubject Specific Sparse Dictionary Learning for Atlas Based Brain MRI (epmc).pdf DeepDyve Pubget Overpricing