MRI resolution enhancement using total variation regularization

Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Volumn , Issue , 2009, Pages 161-164

  Joshi, Shantanu H ^a   Marquina, Antonio ^b   Osher, Stanley J ^a   Dinov, Ivo ^a   Van Horn, John D ^a   Toga, Arthur W ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Edge preserved sampling; Image enhancement; Skull stripping; Total variation

Indexed keywords

ANATOMICAL INFORMATION; BRAIN IMAGES; BRAIN MRI; DECONVOLUTION MODELS; EDGE PRESERVING; EDGE-PRESERVED SAMPLING; NOVEL METHODS; PRE-PROCESSING STEP; RECONSTRUCTION PROBLEMS; RESOLUTION ENHANCEMENT; SKULL STRIPPING; TOTAL VARIATION; TOTAL VARIATION NORM; TOTAL VARIATION REGULARIZATION; VOLUMETRIC IMAGES;

IMAGE ENHANCEMENT; IMAGE RECONSTRUCTION; REPAIR; SIGNAL RECEIVERS; STRIPPING (DYES);

THREE DIMENSIONAL;

EID: 70449382028     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2009.5193008     Document Type: Conference Paper

References (11)

1. L.M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Comput. Math. and Math. Phys., 7:200-217, 1967.
2. E. Carmi, S. Liu, N. Alon, A. Fiat, and D. Fiat. Resolution enhancement in MRI. Magnetic Resonance Imaging, 24(2):133- 154, 2006.
3. M. Elad and A. Feuer. Restoration of a single super-resolution image from several blurred,noisy, and undersampled measured images. IEEE Tran. Image Processing, 6(12):1646-1658, 1997.
4. H. Greenspan, G. Oz, N. Kiryati, and S. Peled. MRI inter-slice reconstruction. Magnetic Resonance Imaging, 20:437 - 446, 2002.
5. M. Irani and S. Peleg. Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, 53(3):231-239, 1991.
6. A. Marquina and S. J. Osher. Image super-resolution by TVregularizationand Bregman iteration. Journal of Scientific omputing, 37(3):367-382, 2008.
7. S. J. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin. An iterative regularization method for Total Variation-based image restoration. Multiscale Modeling and Simulation, 4(2):460-489, 2005.
8. L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60(1-4):259-268, 1992.
9. D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga. Online resource for segmentation validation. Neuroimage, doi:10.1016/j. neuroimage.2008.10.066.
10. S. M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, 2002.
11. R. Y. Tsai and T. S. Huang. Multi-frame image restoration and registration. Advances in Computer Vision and Image Processing, pages 317-339, 1984.