Spatial regularization of functional connectivity using high-dimensional Markov random fields

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

Volumn 6362 LNCS, Issue PART 2, 2010, Pages 363-370

  Liu, Wei ^a   Zhu, Peihong ^a   Anderson, Jeffrey S ^b   Yurgelun Todd, Deborah ^b   Fletcher, P Thomas ^a  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL CHALLENGES; DETECTION ALGORITHM; ESTIMATION PROBLEM; FUNCTIONAL CONNECTIVITY; GAUSSIAN SMOOTHING; GRAPHICS PROCESSING UNIT; HIGH DIMENSIONALITY; HIGH-DIMENSIONAL; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; ORIGINAL IMAGES; PARALLELIZED ALGORITHM; RESTING STATE; TASK-BASED;

COMPUTER GRAPHICS EQUIPMENT; IMAGE SEGMENTATION; MEDICAL COMPUTING; PROGRAM PROCESSORS;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; BRAIN; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; EVOKED RESPONSE; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NERVE TRACT; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROBABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS;

ALGORITHMS; BRAIN; BRAIN MAPPING; DATA INTERPRETATION, STATISTICAL; EVOKED POTENTIALS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; NEURAL PATHWAYS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84883844026     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15745-5_45     Document Type: Conference Paper

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