A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine

IEEE Transactions on Biomedical Engineering

Volumn 52, Issue 12, 2005, Pages 2041-2057

  Benameur, Said ^a,b   Mignotte, Max ^a,b,e   Labelle, Hubert ^c,f,g   De Guise, Jacques A ^a,d  

^a CENTRE HOSPITALIER DE L UNIVERSITÉ DE MONTRÉAL   (Canada)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

^c UNIVERSITY OF MONTREAL   (Canada)

^d ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

^e DIRO at Computer Vision and Geometric Modeling Laboratory

^f DIRO at Computer Vision and Geometric Modeling Laboratory ^*

^g Motion Sciences Research

Author keywords

3 D reconstruction model; 3 D 2 D registration; Biplanar radiographies; Energy function minimization; Hierarchical statistical modeling; Medical imaging; Scoliosis; Shape model; Stochastic optimization

Indexed keywords

3-D/2-D REGISTRATION; 3D RECONSTRUCTION MODEL; BIPLANAR RADIOGRAPHIES; ENERGY FUNCTION MINIMIZATION; SCOLIOSIS; SHAPE MODEL;

MATHEMATICAL MODELS; MEDICAL IMAGING; NONLINEAR SYSTEMS; OPTIMIZATION; RADIOGRAPHY; STOCHASTIC CONTROL SYSTEMS;

IMAGE RECONSTRUCTION;

ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC ACCURACY; HUMAN; IMAGE RECONSTRUCTION; INTERMETHOD COMPARISON; LUMBAR SPINE; MAJOR CLINICAL STUDY; SCOLIOSIS; SPINE MALFORMATION; STATISTICAL MODEL; THORACIC SPINE; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; IMAGING, THREE-DIMENSIONAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SCOLIOSIS; SENSITIVITY AND SPECIFICITY; SPINE;

EID: 28844476449     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2005.857665     Document Type: Article

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