Document Detail


ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm.
MedLine Citation:
PMID:  19162885     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this paper, a semi-automatic segmentation method for volume assessment of Intestinal-type adenocarcinoma (ITAC) is presented and validated. The method is based on a Gaussian hidden Markov random field (GHMRF) model that represents an advanced version of a finite Gaussian mixture (FGM) model as it encodes spatial information through the mutual influences of neighboring sites. To fit the GHMRF model an expectation maximization (EM) algorithm is used. We applied the method to a magnetic resonance data sets (each of them composed by T1-weighted, Contrast Enhanced T1-weighted and T2-weighted images) for a total of 49 tumor-contained slices. We tested GHMRF performances with respect to FGM by both a numerical and a clinical evaluation. Results show that the proposed method has a higher accuracy in quantifying lesion area than FGM and it can be applied in the evaluation of tumor response to therapy.
Authors:
Katia M Passera; Paolo Potepan; Luca Brambilla; Luca T Mainardi
Related Documents :
20733235 - Model synthesis: a general procedural modeling algorithm.
19880215 - Modeling the arabidopsis seed shape by a cardioid: efficacy of the adjustment with a sc...
15344445 - Shape discrimination in the hippocampus using an mdl model.
20180895 - Accuracy assessment of three-dimensional surface reconstructions of teeth from cone bea...
21563975 - Big cat phylogenies, consensus trees, and computational thinking.
17904675 - A weibullian model for microbial injury and mortality.
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference     Volume:  2008     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2008  
Date Detail:
Created Date:  2009-02-16     Completed Date:  2009-05-05     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1218-21     Citation Subset:  IM    
Affiliation:
Dipartimento di Ingegneria Biomedica, Politecnico di Milano, Italy. katia.passera@polimi.it
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Adenocarcinoma / pathology*
Algorithms*
Humans
Magnetic Resonance Imaging
Markov Chains
Models, Statistical*
Paranasal Sinus Neoplasms / pathology*

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


Previous Document:  Automatic segmentation of intracranial hematoma and volume measurement.
Next Document:  Detection of chromosomal abnormalities with multi-color fluorescence in situ hybridization (M-FISH) ...