Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer's disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ICC=0.96 , SI = 0.62±0.16 for ADC data set and ICC=0.78 , SI=0.51±0.15 for PREVENT-AD data set. The proposed method was robust in the independent sample yielding SI=0.64±0.17 with ICC=0.93 for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.

The analysis of data represented as graphs is common having wide scale applications from social networks to medical imaging. A popular analysis is to cut the graph so that the disjoint subgraphs can represent communities (for social network) or background and foreground cognitive activity (for medical imaging). An emerging setting is when multiple data sets (graphs) exist which opens up the opportunity for many new questions. In this paper we study two such questions: i) For a collection of graphs find a single cut that is good for all the graphs and ii) For two collections of graphs find a single cut that is good for one collection but poor for the other. We show that existing formulations of multiview, consensus and alternative clustering cannot address these questions and instead we provide novel formulations in the spectral clustering framework. We evaluate our approaches on functional magnetic resonance imaging (fMRI) data to address questions such as: "What common cognitive network does this group of individuals have?" and "What are the differences in the cognitive networks for these two groups?" We obtain useful results without the need for strong domain knowledge.

Spectral clustering is often reported in the literature as successfully being applied to applications from image segmentation to community detection. However, what is not reported is that great time and effort are required to construct a graph Laplacian to achieve these successes. This problem which we call Laplacian construction is critical for the success of spectral clustering but is not well studied by the community. Instead the best Laplacian is typically learnt for each domain from trial and error. This is problematic for areas such as medical imaging since: (i) the same images can be segmented in multiple ways depending on the application focus and (ii) we don't wish to construct one Laplacian, rather we wish to create a method to construct a Laplacian for each patient's scan. In this paper we attempt to automate the process of Laplacian creation with the help of guidance towards the application focus. In most domains creating a basic Laplacian is plausible, so we propose adjusting this given Laplacian by discovering important nodes. We formulate this problem as an integer linear program with a precise geometric interpretation which is globally minimized using large scale solvers such as Gurobi. We show the usefulness on a real world problem in the area of fMRI scan segmentation where methods using standard Laplacians perform poorly.

We pose the problem of network discovery which involves simplifying spatio-temporal data into cohesive regions (nodes) and relationships between those regions (edges). Such problems naturally exist in fMRI scans of human subjects. These scans consist of activations of thousands of voxels over time with the aim to simplify them into the underlying cognitive network being used. We propose supervised and semi-supervised variations of this problem and postulate a constrained tensor decomposition formulation and a corresponding alternating least squares solver that is easy to implement. We show this formulation works well in controlled experiments where supervision is incomplete, superfluous and noisy and is able to recover the underlying ground truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals. Finally, we show that the reconstruction error of the decomposition provides a useful measure of the network strength and is useful at predicting key cognitive scores both by itself and with clinical information.

Tensor-based morphometry is a powerful tool for automatically computing longitudinal change in brain structure. Because of bias in images and in the algorithm itself, however, a penalty term and inverse consistency are needed to control the over-reporting of nonbiological change. These may force a tradeoff between the intrinsic sensitivity and specificity, potentially leading to an under-reporting of authentic biological change with time. We propose a new method incorporating prior information about tissue boundaries (where biological change is likely to exist) that aims to keep the robustness and specificity contributed by the penalty term and inverse consistency while maintaining localization and sensitivity. Results indicate that this method has improved sensitivity without increased noise. Thus it will have enhanced power to detect differences within normal aging and along the spectrum of cognitive impairment.

Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.

This paper presents a technique for shape-based recognition that fuses pixellevel and object-level approaches into a unified framework. A pixel-level algorithm classifies individual pixels as belonging to a target object or clutter based on automatically-selected shape features computed in a spatial arrangement around them; an object-level algorithm classifies object-sized rectangular image regions as objects or clutter by aggregating pixel classifier scores in the regions. We train a cascade of interleaved pixel-level and objectlevel modules to quickly localize complex-shaped objects in highly cluttered scenes under arbitrary out-of-image-plane rotation. Experimental results on a large set of real, highly-cluttered images of a common object under arbitrary out of image plane rotation demonstrate improvements over cascades of strictly pixel-level modules. 1

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge cues. We first use example images of the desired object in typical backgrounds to train a classifier cascade which determines whether edge pixels in an image belong to an instance of the object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels. The features used for this classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of complex objects in cluttered indoor scenes under arbitrary out-of-image-plane rotation.

We frame the problem of object recognition from edge cues in terms of determining whether individual edge pixels belong to the target object or to clutter, based on the configuration of edges in their vicinity. A classifier solves this problem by computing sparse, localized edge features at image locations determined at training time. In order to save computation and solve the aperture problem, we apply a cascade of these classifiers to the image, each of which computes edge features over larger image regions than its predecessors. Experiments apply this approach to the recognition of real objects with holes and wiry components in cluttered scenes under arbitrary out-of-image-plane rotation.

We present techniques for building models of complex environments from range data gathered at multiple viewpoints. The challenges in this problem are: the matching of unregistered views without prior knowledge of pose, the use of very large data sets, and the manipulation of data sets of different resolutions and from different sensors. Our approach is unique in that no prior knowledge of the relative viewpoints is needed in order to register the data. We show results in building maps of interior environment from range finding data, building large terrain maps from ground-based and from aerial data, and from an operational for mapping from stereo data for hazardous environment characterization. The paper summarizes the major results obtained so far in this area.

Presents a method for quickly filtering range data points to make object recognition in large 3D data sets feasible. The general approach, called "3D cueing", uses shape signatures from object models as the basis for a fast, probabilistic classification system which rates scene points in terms of their likelihood of belonging to a model. This algorithm which could be used as a front-end for any traditional 3D matching technique, is demonstrated using several models and cluttered scenes in which the model occupies between 1% and 50% of the data points.

We present a method for building models of complex environments from range data gathered at multiple viewpoints. Our approach is unique in that no prior knowledge of the relative positions of the viewpoints is needed in order to register data from them. Furthermore, we present a technique for specification and utilization of so-called "common-sense" constraints on the transformations between views to improve the accuracy and speed of the registration process. Results are shown from our effort to map a 60 m by 20 m multiple-room storage area containing a cluttered array of objects.