Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population.

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Abstract

The increasing availability of MRI brain data opens up a research direction for abnormality detection which is necessary to on-time detection of impairment and performing early diagnosis. The paper proposes scores based on z-score transformation and kernel density estimation (KDE) which are respectively Gaussian-based assumption and nonparametric modeling to detect the abnormality in MRI brain images. The methodologies are applied on gray-matter-based score of Voxel-base Morphometry (VBM) and sparse-based score of Sparse-based Morphometry (SBM). The experiments on well-designed normal control (CN) and Alzheimer disease (AD) subsets extracted from MRI data set of Alzheimer’s Disease Neuroimaging Initiative (ADNI) are conducted with threshold-based classification. The analysis of abnormality percentage of AD and CN population is carried out to validate the robustness of the proposed scores. The further cross validation on Linear discriminant analysis (LDA) and Support vector machine (SVM) classification between AD and CN show significant accuracy rate, revealing the potential of statistical modeling to measure abnormality from a population of normal subjects.
Original languageEnglish
Title of host publicationProceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsNadia Magnenat-Thalmann, Paul Richard, Lars Linsen, Alexandru Telea, Sebastiano Battiato, Francisco Imai, José Braz
PublisherSciTePress
Pages256-263
Number of pages8
Volume3
ISBN (Print)978-989-758-175-5
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP - Rome, Italy
Duration: 27 Feb 201629 Feb 2016

Conference

Conference11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP
Abbreviated titleVISIGRAPP 2016
Country/TerritoryItaly
CityRome
Period27/02/1629/02/16

Keywords

  • Alzheimer
  • Normality Map
  • Classification
  • Sparse-based

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