The development of non-invasive techniques for the early diagnosis of Alzheimer’s disease are of high importance facing the growing number of affected persons and the immense cost to our society caused be dementia. Computer based analysis of FDG-PET scans might become a possibility to make early diagnosis more efficient. Temporal lobe is the the main location of medical findings. We have clues that in PET images the parietal lobe contains more information about Alzheimer’s disease. We used a probabilistic brain atlas to include prior information about the localization of changes in the human brain, classified Alzheimer’s disease versus normal control group and got better classification rates focusing on parietal lobe.

In this work we present a novel algorithm for nonnegative tensor factorization (NTF). Standard NTF algorithms are very restricted in the size of tensors that can be decomposed. Our algorithm overcomes this size restriction by interpreting the tensor as a set of sub-tensors and by proceeding the decomposition of sub-tensor by sub-tensor. This approach requires only one sub-tensor at once to be available in memory. (C) 2017 Elsevier B.V. All rights reserved.

The analysis of positron emission tomography (PET) scan image is challenging due to a high level of noise and a low resolution and also because differences between healthy and demented are very subtle. High dimensional classification methods based on PET have been proposed to automatically discriminate between normal control group (NC) patients and patients with Alzheimer's disease (AD), with mild cognitive impairment (MCI), and mild cognitive impairment converting to Alzheimer's disease (MCIAD ) (a group of patients that clearly degrades to AD). We developed a voxelbased method for volumetric image analysis. We performed 3 classification experiments AD vs CG, AD vs MCI, MCIAD vs MCI. We will also give a small demonstration of the presented method on a set of face images. This method is capable to extract information about the location of metabolic changes induced by Alzheimer's disease that directly relies statistical features and brain regions of interest (ROIs). We produce "maps" to visualize the most informative regions of the brain and compare them with voxel-wise statistics. Using the mean intensity of about 2000 6 × 6 × 6mm patches, selected by the extracted map, as input for a classifier we obtain a classification rate of 95.5%.

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into several classes depending on the problem at hand. The study investigates the potential use of 2D EEMD in combination with proper classifiers to form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from a dementia are taken to illustrate this ability.

In this thesis we want to explore novel image analysis methods for the early detection of metabolic changes in the human brain caused by Alzheimer's disease (AD). We will present two methodological contributions and present their application to a real life data set. We present a machine learning based method to create a map of local distribution of classification relevant information in an image set. The presented method can be applied using different image characteristics which makes it possible to adapt the method to many kinds of images. The maps generated by this method are very localized and fully consistent with prior findings based on Voxel wise statistics. Further we preset an algorithm to draw a sample of patches according to a distribution presented by means of a map. Implementing a patch based classification procedure using the presented algorithm for data reduction we were able to significantly reduce the amount of patches that has to be analyzed in order to obtain good classification results. We present a novel non-negative tensor factorization (NTF) algorithm for the decomposition of large higher order tensors. This algorithm considerably reduces memory consumption and avoids memory overhead. This allows the fast decomposition even of tensors with very unbalanced dimensions. We apply this algorithm as feature extraction method in a computer-aided diagnosis (CAD) scheme, designed to recognize early-stage ad and mild cognitive impairment (MCI) using fluorodeoxyglucose (FDG) positron emission tomography (PET) scans only. We achieve state of the art classification rates.

Statistical methods are increasingly used in the analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. We will demonstrate a method to extract information about the location of metabolic changes induced by Alzheimer’s disease based on a machine learning approach that directly relies features and brain areas to search for regions of interest (ROIs). This approach has the advantage over voxel-wise statistics to consider also the interactions between the features/voxels. We produce “maps” to visualize the most informative regions of the brain and compare the maps created by our approach with voxel-wise statistics. In classification experiments, using the extracted maps, we achieved classification rates of up to 95.5%.

The early diagnostic of Alzheimer disease by non-invasive technique becomes a priority to improve the life of patient and his social environment by an adapted medical follow-up. This is a necessity facing the growing number of affected persons and the cost to our society caused by dementia. Computer based analysis of Fluorodeoxyglucose PET scans might become a possibility to make early diagnosis more efficient. Temporal and parietal lobes are the main location of medical findings. We have clues that in PET images these lobes contain more information about Alzheimer's disease. We used a texture operator, the Local Binary Pattern, to include prior information about the localization of changes in the human brain. We use a Support Vector machine (SVM) to classify Alzheimer's disease versus normal control group and to get better classification rates focusing on parietal and temporal lobes.

Exploratory matrix factorization (EMF) techniques applied to two-way or multi-way biomedical data arrays provide new and efficient analysis tools which are currently explored to analyze large scale data sets like gene expression profiles (GEP) measured on microarrays, lipidomic or metabolomic profiles acquired by mass spectrometry (MS) and/or high performance liquid chromatography (HPLC) as well as biomedical images acquired with functional imaging techniques like functional magnetic resonance imaging (fMRI) or positron emission tomography (PET). Exploratory feature extraction techniques like, for example, Principal Component Analysis (PCA), Independent Component Analysis (ICA) or sparse Nonnegative Matrix Factorization (NMF) yield uncorrelated, statistically independent or sparsely encoded and strictly non-negative features which in case of GEPs are called eigenarrays (PCA), expression modes (ICA) or metagenes (NMF). They represent features which characterize the data sets under study and are generally considered indicative of underlying regulatory processes or functional networks and also serve as discriminative features for classification purposes. In the latter case, EMF techniques, when combined with diagnostic a priori knowledge, can directly be applied to the classification of biomedical data sets by grouping samples into different categories for diagnostic purposes or group genes, lipids, metabolic species or activity patches into functional categories for further investigation of related metabolic pathways and regulatory or functional networks. Although these techniques can be applied to large scale data sets in general, the following discussion will primarily focus on applications to microarray data sets and PET images.

Features are extracted from PET images employing exploratory matrix factorization techniques, here non-negative matrix factorization (NMF). Appropriate features are fed into classifiers such as support vector machine or random forest. An automatic classification is achieved with high classification rate and only few false negatives.