Parasites of wild rabbits (Oryctolagus cuniculus) from an urban area in Germany, in relation to worldwide results.

The European rabbit (Oryctolagus cuniculus) belongs to the most invasive and successful mammalian species, which is distributed nearly worldwide. In Europe, they inhabit broad parts of the mainland and subsequently reached several European islands via anthropogenic diversion. Rabbits can also serve as hosts for numerous parasite species.

Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.

We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images.

Random forest-based similarity measures for multi-modal classification of Alzheimer's disease.

Neurodegenerative disorders, such as Alzheimer's disease, are associated with changes in multiple neuroimaging and biological measures. These may provide complementary information for diagnosis and prognosis. We present a multi-modality classification framework in which manifolds are constructed based on pairwise similarity measures derived from random forest classifiers.

Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease.

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning.