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In this paper, we present a new metric combining regional measurements to improve image based population studies that use manifold learning techniques. These studies currently rely on a single score over the whole brain image domain. Thus, they require large amount of training data to uncover spatially complex variation in the whole brain impacted by diseases. We reduce the impact of this issue by...
This paper presents a new method for segmentation of ambiguously defined structures, such as the hippocampus, by exploiting prior knowledge from another perspective. An expert's experience of where to use prior knowledge and where image information, is captured as a local weighting map. This map can be used to locally guide the evolution in a level set evolution framework. Such a map is produced for...
Hippocampal atrophy is a well-known characteristic associated with Alzheimer's disease. In this work, we propose a 4D Expectation Maximization framework for measuring the atrophy rate of the hippocampus from serial magnetic resonance images. One novelty of the framework is a disease-specific prior that regularizes the segmentation near the borders of the hippocampus. Regions where the hippocampus...
Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator which is also used in this paper for hippocampal shape comparison. We proposed a feature vector which consists of size measures and shape descriptors based on Laplace Beltrami eigenvalues and eigenfunctions. The aforementioned feature space is utilised...
In recent years, multi-atlas segmentation has emerged as one of the most accurate techniques for the segmentation of brain magnetic resonance (MR) images, especially when combined with intensity-based refinement techniques such as graph-cut or expectation-maximization (EM) optimization. However, most of the work so far has focused on intensity-based refinement strategies for individual anatomical...
Multi-atlas label fusion has been widely applied in medical image analysis. To reduce the bias in label fusion, we proposed a joint label fusion technique to reduce correlated errors produced by different atlases via considering the pair-wise dependencies between them. Using image similarities from image patches to estimate the pairwise dependencies, we showed promising performance. To address the...
Multimodality classification of Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant...
In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We...
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