6533b85dfe1ef96bd12bdf89
RESEARCH PRODUCT
MRI resolution enhancement using total variation regularization
Ivo D. DinovArthur W. TogaAntonio MarquinaJohn D. Van HornShantanu H. JoshiStanley Oshersubject
Decimationmedicine.diagnostic_testbusiness.industryComputer scienceMagnetic resonance imagingIterative reconstructionImage segmentationTotal variation denoisingArticleComputer Science::Computer Vision and Pattern RecognitionNorm (mathematics)medicineComputer visionSegmentationArtificial intelligenceDeconvolutionAnisotropybusinessImage resolutiondescription
We propose a novel method for resolution enhancement for volumetric images based on a variational-based reconstruction approach. The reconstruction problem is posed using a deconvolution model that seeks to minimize the total variation norm of the image. Additionally, we propose a new edge-preserving operator that emphasizes and even enhances edges during the up-sampling and decimation of the image. The edge enhanced reconstruction is shown to yield significant improvement in resolution, especially preserving important edges containing anatomical information. This method is demonstrated as an enhancement tool for low-resolution, anisotropic, 3D brain MRI images, as well as a pre-processing step to improve skull-stripping segmentation of brain images.
year | journal | country | edition | language |
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2009-06-01 | 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro |