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In this paper, we propose some new features that are based on the correlation of deformation vectors of two or more brain structures when one MRI brain volume (the Template image) is transformed into another volume (the Study image) by a Computational Anatomy method. Thenew features can reveal the deformation relationship between two different structures in a quantitative way. We also use vector classification methods to distinguish Normal subjects (NL) from Alzheimer Disease (AD) patients using combined sets of known and new features ,and apply these methods to a set of MRI volumes from the ADNI database. Using these methods we obtain a good correspondence between theclassification outcomes and the ground truth data. Also, we visualize our data and results in a specially designed user interface. Conclusion:In this paper, we have defined a new set of parameters for the analysis of evolution of neurodegenerative diseases based on MR images. In order to be able to visualize the deformation and investigate the usefulness of these parameters, we developed a visualizationenvironment that displays and evaluates them, and helps to select the regions for which they should be calculated, and subsequently applied vector classification methods to monitor whether AD patients could be discerned from NL subjects. We have tested this on a data setfrom the ADNI database. Our results show that these parameters do indeed indicate differences between AD and NL subjects. From the cross validation result, we find that the Directional Correlation Coefficient of the ventricle and hippocampus, Mean Jacobian Displacement Correlation of the ventricle and hippocampus and Ventricle Region size are three key features that are most promising as parameters todistinguish AD patients from NL subjects. Of the three considered methods, the SVM classification method is the best method to make anautomatic classification for this task.

We propose in this paper an approach to quantifying the rate of atrophy of the brain of patients with Alzheimer’s disease. This approachis based on Computational Anatomy which al- lows the computation ofintermediate MR brain volumes be- tween the ones of regular scans.This increases dramatically the granularity of brain structure information, without requir- ing extra scans. We define two spaces: (i) the joint brain tissue deformation displacement vector magnitude andJacobian, (ii) the joint polar angles of the displacement vector. The shape of the distribution patterns in both spaces allow us to: (i)quan- tify atrophy rates of specific brain structures, such as, theven- tricles and the hippocampus, (ii) to build up models for the in- terpolation and extrapolation of atrophy rate parameters. The novelty of this approach is that it allows us to interpolate and extrapolate atrophy rate parameters computed from the two spaces, and thusderive precise models for patient diagnosis and/or prognosis. We tested this approach on a set of ADNI patients with diagnosed Alzheimer’s disease, mild cognitive impairment, and normal controls. This approach could also be used in the diagnosis of patients with other neurodegener- ative diseases, such as, frontal lobe dementia and Schizophre- nia.