Calculation of the confidence intervals for transformation parameters in the registration of medical images

Medical Image Analysis

Volumn 13, Issue 2, 2009, Pages 215-233

  Bansal, Ravi ^a,b   Staib, Lawrence H ^d   Laine, Andrew F ^c   Xu, Dongrong ^a,b   Liu, Jun ^a,b   Posecion, Lainie F ^b   Peterson, Bradley S ^a,b  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b NEW YORK STATE PSYCHIATRIC INSTITUTE   (United States)

^c Yale University ^*  (United States)

^d YALE UNIVERSITY   (United States)

Author keywords

Confidence region; Mutual information; Nonlinear least squares; Similarity transformation

Indexed keywords

ANATOMICAL FEATURES; CONFIDENCE INTERVALS; CONFIDENCE REGION; DATA SETS; ESTIMATED PARAMETERS; FUNCTIONAL RELATIONS; GAUSSIAN DISTRIBUTED; GLOBAL SCALE; LEAST-SQUARES ESTIMATIONS; MATHEMATICAL FRAMEWORKS; MEDICAL IMAGES; MISREGISTRATION; MUTUAL INFORMATION; NONLINEAR LEAST-SQUARES; QUANTITATIVE ASSESSMENTS; REGISTRATION ERRORS; SIMILARITY PARAMETERS; SIMILARITY TRANSFORMATION; TECHNOLOGICAL LIMITATIONS; TRANSFORMATION PARAMETERS;

COMPUTATIONAL FLUID DYNAMICS; COVARIANCE MATRIX; IMAGE REGISTRATION; RANDOM VARIABLES; TRELLIS CODES;

MATHEMATICAL TRANSFORMATIONS;

ACCURACY; ALGORITHM; ANALYTIC METHOD; ANATOMY; ARTICLE; CONFIDENCE INTERVAL; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE PROCESSING; IMAGE QUALITY; IMAGING SYSTEM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; MEDICAL TECHNOLOGY; MULTIVARIATE ANALYSIS; NEUROANATOMY; NEUROIMAGING; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; STATISTICAL PARAMETERS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CONFIDENCE INTERVALS; CORPUS CALLOSUM; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 62749197565     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2008.09.002     Document Type: Article

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