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Methods for Large Volume Image Analysis
Zitatschlüssel Kodewitz2013
Autor Kodewitz, Andreas
Jahr 2013
Schule Universite d’Evry
Zusammenfassung 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 classifi- cation rates. In the context of these image analysis tasks we present our data source, scan selection and preprocessing. The key aspects we want to consider are the volumetric nature of the data, prior information available about the localization of metabolic changes, discovering the localization of metabolic changes from the data, using this information to reduce the amount of data to be analyzed and discovering discriminant features from the data. The presented methods provide precise information about the localization of metabolic changes and classification rates of up to 92.6 % for early AD and 83.8 % for MCI. Furthermore, we are capable to separate stable MCI patients from MCI patients declining to AD within 2 years after acquisition of the PET scan with a classification rate of 84.7 %. These are important steps toward a reliable early detection of AD.
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