Document Detail


Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models.
MedLine Citation:
PMID:  16235657     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this paper, we present an original method for the three-dimensional (3-D) reconstruction of the scoliotic rib cage from a planar and a conventional pair of calibrated radiographic images (postero-anterior with normal incidence and lateral). To this end, we first present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers (PPCA). This method is based on the stochastic expectation maximization (SEM) algorithm. Parameters of this mixture model are used to constrain the 3-D biplanar reconstruction problem of scoliotic rib cage. More precisely, the proposed PPCA mixture model is exploited for dimensionality reduction and to obtain a set of probabilistic prior models associated with each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood, for each considered class-conditional prior model, the proposed 3-D reconstruction is stated as an energy function minimization problem, which is solved with an exploration/selection algorithm. The optimal 3-D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3-D method of reconstruction has been successfully tested and validated on a database of 20 pairs of biplanar radiographic images of scoliotic patients, yielding very promising results. As an alternative to computed tomography-scan 3-D reconstruction this scheme has the advantage of low radiation for the patient, and may also be used for diagnosis and evaluation of deformity of a scoliotic rib cage. The proposed method remains sufficiently general to be applied to other reconstruction problems for which a database of objects to be reconstructed is available (with two or more radiographic views).
Authors:
Said Benameur; Max Mignotte; Fran?ois Destrempes; Jacques A De Guise
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Publication Detail:
Type:  Clinical Trial; Journal Article; Research Support, Non-U.S. Gov't; Validation Studies    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  52     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2005 Oct 
Date Detail:
Created Date:  2005-10-20     Completed Date:  2005-11-15     Revised Date:  2010-04-12    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1713-28     Citation Subset:  IM    
Affiliation:
Laboratoire de recherche en imagerie et orthop?die, University of Montr?al Hospital Research Centre, Montr?al, QC H2L 2W5, Canada. benameus@iro.umontreal.ca
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artificial Intelligence
Computer Simulation
Humans
Imaging, Three-Dimensional / methods*
Models, Biological*
Models, Statistical
Principal Component Analysis
Radiographic Image Enhancement / methods*
Radiographic Image Interpretation, Computer-Assisted / methods*
Ribs / radiography*
Scoliosis / radiography*
Subtraction Technique
Systems Integration

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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