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

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.