Hybrid formulation of the model-based non-rigid registration problem to improve accuracy and robustness

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

Volumn 3750 LNCS, Issue , 2005, Pages 295-302

  Clatz, Olivier ^a,b   Delingette, Hervé ^a   Talos, Ion Florin ^b   Golby, Alexandra J ^b   Kikinis, Ron ^b   Jolesz, Ferenc A ^b   Ayache, Nicholas ^a   Warfield, Simon K ^c  

^a INRIA   (France)

^b HARVARD MEDICAL SCHOOL   (United States)

^c BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; BRAIN; FINITE ELEMENT METHOD; ITERATIVE METHODS; LEAST SQUARES APPROXIMATIONS; MAGNETIC RESONANCE IMAGING;

BIOMECHANICAL MODEL; DISPLACEMENT FIELD; REGISTRATION PROCESS; ROBUST ESTIMATION;

ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; BRAIN; BRAIN TUMOR; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; FINITE ELEMENT ANALYSIS; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PATHOPHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN NEOPLASMS; COMPUTER SIMULATION; FINITE ELEMENT ANALYSIS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MODELS, BIOLOGICAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 33744804585     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11566489_37     Document Type: Conference Paper

References (7)

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