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Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed...
We present the first use of multi-region FDG-PET data for classification of subjects from the Alzheimer's Disease Neuroimaging Initiative. Image data were obtained from 69 healthy controls, 71 AD patients, and 147 patients with a baseline diagnosis of MCI. Anatomical segmentations were automatically generated in the native MRI-space of each subject, and the mean signal intensity per cubic millimetre...
We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains,...
Automatic segmentation of white matter hyperintensities (WMH) from T2-Weighted and FLAIR MRI is a common task that needs to be performed in the analysis of many different diseases. A method to segment the WMH is proposed whereby a local intensity model (LIM) of normal tissue is generated. WMH are detected as outliers from this model. The LIM enables an accurate modeling of intensity variations thus...
We introduce a class of spectral shape signatures constructed from symmetric functions on the eigenfunctions of the Laplacian exponentially weighted by their eigenvalues. Such a construction is motivated by problems that arise in the use of the eigenfunctions for shape comparison, such as indeterminacies in the choice of signs and the particular ordering in which the eigenfunctions are presented....
This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative database. T1-weighted images (1.5 T and 3 T) have been processed with the multi-atlas based MAPER...
Recent work suggests that the space of brain magnetic resonance (MR) images can be described by a nonlinear and low-dimensional manifold. In the context of classifying Alzheimer's disease (AD) patients from healthy controls, we propose a method to incorporate subject meta-information into the manifold learning step. Information such as gender, age or genotype is often available in clinical studies...
Whole brain extraction is an important pre-processing step in neuro-image analysis. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to...
This paper shows a machine learning approach based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) to compare the diagnostic accuracy on very early Alzheimer's Disease (AD) patients with 18F FDG and Pittsburg Compound B (PiB) PET imaging. The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset is used for testing, making use of the longitudinal character. Mild Cognitive...
There is an unmet medical need for identifying neuroimaging biomarkers for Alzheimer's disease (AD), the most common form of senile dementia. These biomarkers are essential for early and accurate diagnosis of AD, monitoring of AD progression, and assessment of AD-modifying therapies. In volumetric studies of the medial temporal lobe and hippocampus, magnetic resonance imaging (MRI), as a technique...
In this work, we present a probabilistic information fusion approach for the diagnosis of dementia from cross-sectional magnetic resonance (MR) images. The approach relies on first mapping the outputs of a support vector classifier (SVM) trained on image features to probabilities and then on combining these probabilities with the class-conditional distributions of neuropsychiatric test scores, such...
The accuracy of scan-to-atlas registration highly depends on the number of landmarks and the precision of landmark identification. An extended landmark, cerebellum inferior (CBI), is introduced in this paper. The extracted brain and midsagittal plane are applied to identify the modified Talairach landmarks and the new introduced landmark CBI. The AC-PC plane is firstly determined and then anatomical...
Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. This paper shows a novel computer aided diagnosis (CAD) system for the early Alzheimer's disease using single photon emission computed tomography (SPECT) images. The proposed system combines a partial least square (PLS) regression model for feature extraction and a random forest predictor...
Computer-assistance has reached virtually every domain within the field of medical imaging. But, even after four decades of intensive medical image analysis research, most of the fully automated methods have not been adopted for clinical routine use. Dedicated computer aided-diagnosis tools with proven clinical impact exist for a narrow range of applications, including mammography and chest imaging,...
There is an urgent need for neuroimaging biomarkers of Alzheimer's disease (AD) that correlate with cognitive decline, and with accepted measures of pathology detectable in cerebrospinal fluid (CSF). Ideal biomarkers should also be able to predict future decline, and should be computable automatically from hundreds to thousands of images without user intervention. Here we used our multi-atlas fluid...
Most computer-aided diagnosis systems for Alzheimer's disease (AD) using magnetic resonance (MR) imaging were based on morphological image features, not functional image features, which would be also useful for diagnosis of AD. The aim of this study was to develop a computer-aided classification system for AD patients based on functional image features derived from the cerebral blood flow (CBF) maps...
In neuroimaging studies, several imaging modalities are commonly used together, but the images are often analyzed separately for different modalities. To address this, I present two massively univariate statistical methods to analyze functional and structural brain imaging data together on voxel-by-voxel basis. In the first example, a permutation-based nonparametric method is used on imaging data...
Schizophrenia, Alzheimer's disease, Parkinson's disease, and other neuropsychiatric degenerative disorders and dementias impose an enormous economic and psychosocial burden on society, communities, and families. In order to gain a better understanding of gene-brain-behavior relationships, improve treatment, and find cures for these diseases, translational research must be conducted with clinical trials...
Multimodality MR image processing and analysis aims to determine to what extent information from various imaging modalities is redundant or complementary and how changes in various regions of the brain, detected by various modalities, interact with each other to produce cognitive and functional changes. Here, we presented a multimodality image processing framework to integrate unique and complementary...
The study of the molecular mechanisms involved in neurite outgrowth and differentiation, requires essential accurate and reproducible segmentation and quantification of neuronal processes. The common method used in this study is to detect and trace individual neurites, i.e. neurite tracing. The challenge comes mainly from the morphological problem in which these images contain ambiguities such as...
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