Brain atrophy from structural magnetic resonance images (MRIs) is widely used as an imaging surrogate marker for Alzheimers disease. Their utility has been limited due to the large degree of variance and subsequently high sample size estimates. The only consistent and reasonably powerful atrophy estimation methods has been the boundary shift integral (BSI). In this paper, we first propose a tensor-based morphometry (TBM) method to measure voxel-wise atrophy that we combine with BSI. The combined model decreases the sample size estimates significantly when compared to BSI and TBM alone.
It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result
hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of
mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making
shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148
AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The
hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary
images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic
basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM)
classification. A wrapper based feature selection method was used as this considers the utility of features in
discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set
of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is
88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature
sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data
indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.
Alzheimer's disease (AD) severely affects the hippocampus: it loses mass and shrinks as the disease advances.
Thus delineation of the hippocampus is an important task in the clinical study of AD. Because of its simplicity
and good performance, multi-atlas based segmentation has become a popular approach for medical image
segmentation. We propose to use manifold learning for atlas selection in the framework of multi-atlas based
segmentation. The framework only benefits when selecting atlases similar to the target image. Since manifold
learning assigns each image a coordinate in low-dimensional space by respecting the neighborhood relationship,
it is well suited for atlas selection. The key contribution is that we use manifold learning based on a metric
derived from non-rigid transformation as the resulting embedding better captures deformations or shape differences
between images than similarity measures based on voxel intensity. The proposed method is evaluated in
a leave-one-out experiment on a set of 110 hippocampus images; we report mean Dice score of 0.9114 (0.0227).
The method was validated against a state-of-the-art method for hippocampus segmentation.