A local mutual information guided denoising technique and its application to self-calibrated partially parallel imaging

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5242 LNCS, Issue PART 2, 2008, Pages 939-947

  Guo, Weihong ^a   Huang, Feng ^b  

^a UNIVERSITY OF ALABAMA   (United States)

^b Invivo Corporation   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EDGE DETECTION; IMAGE DENOISING; IMAGE RECONSTRUCTION; IMAGE RESOLUTION; ITERATIVE METHODS; LINEAR MATRIX INEQUALITIES; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING;

DE-NOISING TECHNIQUES; HIGH SIGNALTO-NOISE RATIOS (SNR); HIGH SPATIAL RESOLUTION; MUTUAL INFORMATIONS; NOISE DISTRIBUTION; PARALLEL ACQUISITION; SENSITIVITY ENCODING; WEIGHTED TOTAL VARIATIONS;

SIGNAL TO NOISE RATIO;

ALGORITHM; ARTICLE; ARTIFACT; AUTOMATED PATTERN RECOGNITION; BRAIN; CALIBRATION; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFACTS; BRAIN; CALIBRATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 58849160874     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85990-1_113     Document Type: Conference Paper

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