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Brain abnormalities such as white matter lesions (WMLs) are not only linked to cerebrovascular disease, but also with normal aging, diabetes and other conditions increasing the risk for cerebrovascular pathologies. Obtaining quantitative measures which assesses the degree or probability of WML in patients is important for evaluating disease burden, and for evaluating its progression and response to interventions. In this paper, we introduce a novel approach for detecting the presence of WMLs in periventricular areas of the brain using manifold-constrained embeddings. The proposed method uses locally linear embedding (LLE) to create "normality" distributions in 12 locations of the brain where deviations from the manifolds are estimated by calculating geodesic distances along locally linear planes in the embedding. A smooth mapping function approximating the relationship between ambient and manifold spaces as a joint distribution maps unseen test images in the intrinsic space. We create a set of low-dimensional embeddings from 876 patches of healthy tissue in 73 subjects and test it on 396 patches imaging both WML and healthy areas in 33 subjects with diabetes. Experiments highlight the need of nonlinear techniques to learn the studied data with detection rates over 85% in true-positives, and the relevance of the computed distance for comparing individuals to a specific pathological pattern.

The detection and monitoring of brain lesions caused by multiple sclerosis is commonly performed with the use of magnetic resonance imaging. Analysis of a large number of images is a time-consuming challenge to the neuroradiologist, that can be accelerated with the assistance of computer-detection software. In 98 baseline and follow-up brain magnetic resonance studies from 88 patients with a diagnosis of multiple sclerosis, we employed locally developed lesion-detection software to assess temporal change in the load of brain lesions and compared its results to routine clinical reports. Analyzing the differences between the follow-up study and the baseline study, the software displays the results in the form of a scrollable axial volume, with the changed lesions highlighted in different colors and superimposed on the baseline reference scan. Although disagreements between the software and the clinical readers in the detection of changed lesions were observed only in 12 (12.2%) cases, the difference reached statistical significance (p=0.04). The mean interpretation time with assistance of the software was 2.7±2.2 minutes. We conclude that the performance of the software-assisted interpretation in the analysis of change over time in multiple sclerosis brain lesions is different from the performance of clinical readers, with a possibly shorter assessment time. The software detected more changes from baseline than clinical readers, suggesting a higher sensitivity, which will have to be confirmed on further analysis.

This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer's disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.

We propose an automated method to segment cortical necrosis from brain FLAIR-MR Images. Cortical necrosis are regions of dead brain tissue in the cortex caused by cerebrovascular disease (CVD). The accurate segmentation of these regions is difficult as their intensity patterns are similar to the adjoining cerebrospinal fluid (CSF). We generate a model of normal variation using MR scans of healthy controls. The model is based on the Jacobians of warps obtained by registering scans of normal subjects to a common coordinate system. For each patient scan a Jacobian is obtained by warping it to the same coordinate system. Large deviations between the model and subject-specific Jacobians are flagged as `abnormalities'. Abnormalities are segmented as cortical necrosis if they are in the cortex and have the intensity profile of CSF. We evaluate our method by using a set of 72 healthy subjects to model cortical variation.We use this model to successfully detect and segment cortical necrosis in a set of 37 patients with CVD. A comparison of the results with segmentations from two independent human experts shows that the overlap between our approach and either of the human experts is in the range of the overlap between the two human experts themselves.