Structural and functional brain images are playing an important role in helping us understand the changes associated with neurological disorders such as Alzheimer's disease (AD). Recent efforts have now started investigating their utility for diagnosis purposes. This line of research has shown promising results where methods from machine learning (such as Support Vector Machines) have been used to identify AD-related patterns from images, for use in diagnosing new individual subjects. In this paper, we propose a new framework for AD classification which makes use of the Linear Program (LP) boosting with novel additional regularization based on spatial "smoothness" in 3D image coordinate spaces. The algorithm formalizes the expectation that since the examples for training the classifier are images, the voxels eventually selected for specifying the decision boundary must constitute spatially contiguous chunks, i.e., "regions" must be preferred over isolated voxels. This prior belief turns out to be useful for significantly reducing the space of possible classifiers and leads to substantial benefits in generalization. In our method, the requirement of spatial contiguity (of selected discriminating voxels) is incorporated within the optimization framework directly. Other methods have made use of similar biases as a pre-or post-processing step, however, our model incorporates this emphasis on spatial smoothness directly into the learning step. We report on extensive evaluations of our algorithm on MR and FDG-PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and discuss the relationship of the classification output with the clinical and cognitive biomarker data available within ADNI.

We study the problem of online subspace learning in the context of sequential observations involving structured perturbations. In online subspace learning, the observations are an unknown mixture of two components presented to the model sequentially -the main effect which pertains to the subspace and a residual/error term. If no additional requirement is imposed on the residual, it often corresponds to noise terms in the signal which were unaccounted for by the main effect. To remedy this, one may impose 'structural' contiguity, which has the intended effect of leveraging the secondary terms as a covariate that helps the estimation of the subspace itself, instead of merely serving as a noise residual. We show that the corresponding online estimation procedure can be written as an approximate optimization process on a Grassmannian. We propose an efficient numerical solution, GOSUS, Grassmannian Online Subspace Updates with Structured-sparsity, for this problem. GOSUS is expressive enough in modeling both homogeneous perturbations of the subspace and structural contiguities of outliers, and after certain manipulations, solvable via an alternating direction method of multipliers (ADMM). We evaluate the empirical performance of this algorithm on two problems of interest: online background subtraction and online multiple face tracking, and demonstrate that it achieves competitive performance with the state-of-the-art in near real time.

We study the cosegmentation problem where the objective is to segment the same object (i.e., region) from a pair of images. The segmentation for each image can be cast using a partitioning/segmentation function with an additional constraint that seeks to make the histograms of the segmented regions (based on intensity and texture features) similar. Using Markov Random Field (MRF) energy terms for the simultaneous segmentation of the images together with histogram consistency requirements using the squared L 2 (rather than L 1) distance, after linearization and adjustments, yields an optimization model with some interesting combinatorial properties. We discuss these properties which are closely related to certain relaxation strategies recently introduced in computer vision. Finally, we show experimental results of the proposed approach.

With the proliferation of wearable cameras, the number of videos of users documenting their personal lives using such devices is rapidly increasing. Since such videos may span hours, there is an important need for mechanisms that represent the information content in a compact form (i.e., shorter videos which are more easily browsable/sharable). Motivated by these applications, this paper focuses on the problem of egocentric video summarization. Such videos are usually continuous with significant camera shake and other quality issues. Because of these reasons, there is growing consensus that direct application of standard video summarization tools to such data yields unsatisfactory performance. In this paper, we demonstrate that using gaze tracking information (such as fixation and saccade) significantly helps the summarization task. It allows meaningful comparison of different image frames and enables deriving personalized summaries (gaze provides a sense of the camera wearer's intent). We formulate a summarization model which captures common-sense properties of a good summary, and show that it can be solved as a submodular function maximization with partition matroid constraints, opening the door to a rich body of work from combinatorial optimization. We evaluate our approach on a new gaze-enabled egocentric video dataset (over 15 hours), which will be a valuable standalone resource.

Our primary interest is in generalizing the problem of Cosegmentation to a large group of images, that is, concurrent segmentation of common foreground region(s) from multiple images. We further wish for our algorithm to offer scale invariance (foregrounds may have arbitrary sizes in different images) and the running time to increase (no more than) near linearly in the number of images in the set. What makes this setting particularly challenging is that even if we ignore the scale invariance desiderata, the Cosegmentation problem, as formalized in many recent papers (except [1]), is already hard to solve optimally in the two image case. A straightforward extension of such models to multiple images leads to loose relaxations; and unless we impose a distributional assumption on the appearance model, existing mechanisms for image-pair-wise measurement of foreground appearance variations lead to significantly large problem sizes (even for moderate number of images). This paper presents a surprisingly easy to implement algorithm which performs well, and satisfies all requirements listed above (scale invariance, low computational requirements, and viability for the multiple image setting). We present qualitative and technical analysis of the properties of this framework.

Numerous studies indicate that the genome of higher eukaryotes is organized into distinct chromosome territories and that the 3-D arrangement of these territories may be closely connected to genomic function and the global regulation of gene expression. Despite this progress, the degree of non-random arrangement remains unclear and no overall model has been proposed for chromosome territory associations. To address this issue, a re-FISH approach was combined with computational analysis to analysis the pair-wise associations for six pairs of human chromosomes (chr #1, 4, 11, 12, 16, 18) in the G(0) state of normal human WI38 lung fibroblast and MCF10A epithelial breast cells. Similar levels of associations were found in WI38 and MCF10A for several of the chromosomes whereas others showed striking differences. A novel computational geometric approach, the generalized median graph (GMG), revealed a preferred probabilistic arrangement distinct for each cell line. Statistical analysis demonstrated that approximately 50% of the associations depicted in the GMG models are present in each individual nucleus. A nearly twofold increase of chromosome 4/16 associations in a malignant breast cancer cell line (MCFCA1a) compared to the related normal epithelial cell line (MCF10A) further demonstrates cancer related changes in chromosome arrangements. Our findings of highly preferred chromosome association profiles that are cell type specific and undergo alterations in cancer cells, lead us to propose a probabilistic chromosome code whereby the 3-D association profile of chromosomes contributes to the functional landscape of the cell nucleus, the global regulation of gene expression and the epigenetic state of chromatin.

There is growing evidence that chromosome territories have a probabilistic non-random arrangement within the cell nucleus of mammalian cells. Other than their radial positioning, however, our knowledge of the degree and specificity of chromosome territory associations is predominantly limited to studies of pair-wise associations. In this study we have investigated the association profiles of eight human chromosome pairs (numbers 1, 2, 3, 4, 6, 7, 8, 9) in the cell nuclei of G(0)-arrested WI38 diploid lung fibroblasts. Associations between heterologous chromosome combinations ranged from 52% to 78% while the homologous chromosome pairs had much lower levels of association (3-25%). A geometric computational method termed the Generalized Median Graph enabled identification of the most probable arrangement of these eight chromosome pairs. Approximately 41% of the predicted associations are present in any given nucleus. The association levels of several chromosome pairs were very similar in a series of lung fibroblast cell lines but strikingly different in skin and colon derived fibroblast cells. We conclude that a large subset of human chromosomes has a preferred probabilistic arrangement in WI38 cells and that the resulting chromosomal associations show tissue origin specificity.

Higher order chromatin organization in concert with epigenetic regulation is a key process that determines gene expression at the global level. The organization of dynamic chromatin domains and their associated protein factors is intertwined with nuclear function to create higher levels of functional zones within the cell nucleus. As a step towards elucidating the organization and dynamics of these functional zones, we have investigated the spatial proximities among a constellation of functionally related sites that are found within euchromatic regions of the cell nucleus including: HP1g, nascent transcript sites (TS), active DNA replicating sites in early S-phase (PCNA) and RNA polymerase II sites. We report close associations among these different sites with proximity values specific for each combination. Analysis of matrin 3 and SAF-A sites demonstrates that these nuclear matrix proteins are highly proximal with the functionally related sites as well as to each other and display closely aligned and overlapping regions following application of the minimal spanning tree (MST) algorithm to visualize higher order network-like patterns. Our findings suggest that multiple factors within the nuclear microenvironment collectively form higher order combinatorial arrays of function. We propose a model for the organization of these functional neighborhoods which takes into account the proximity values of the individual sites and their spatial organization within the nuclear architecture. J. Cell. Biochem. 105: 391-403, 2008. ß 2008 KEY WORDS: RNA POLYMERASE II SITES; HP1g SITES; REPLICATION SITES; TRANSCRIPTION SITES; CELL NUCLEUS; PROLIFERATING CELL NUCLEAR ANTIGEN; NUCLEAR MATRIX; MATRIN 3; SAF-A; COMPUTER IMAGE SEGMENTATION; PROXIMITY ANALYSIS; PATTERN RECOGNITION IMAGE ANALYSIS; MINIMAL SPANNING TREE NETWORKS D espite significant advances in molecular biology and biochemistry, our understanding of the nucleus is at its infancy. The genome is more than a linear sequence of DNA [Misteli, 2007] and is segmented into chromosomes which occupy distinct territories in the interphase cell nucleus [Cremer et al., 2001]. Chromatin within these chromosomes is arranged into multiple levels of hierarchical organization from the nucleosomal 10 nm arrays to the 30 nm fibers to chromatin loops and higher order domains [Berezney, 2002;Cremer et al., 2006;Razin et al., 2007]. Although the eukaryotic nucleus is devoid of any internal membranes, it introduces an incredible level of complexity and several layers of control, which regulate the genomic functions and increase the efficiency of processivity and enzyme regulatory mechanisms. Moreover, the compartmentalization of genomic functions and factors that mediate these functions suggests a high level of structural organization [Berezney et al., 1996;Strouboulis Journal of Cellular Biochemistry ARTICLE Journal of Cellular Biochemistry 105: 391-403 (2008) 391Abbreviations used: DAPI, 4 0 ,6-diamidino-2-phenylindole; HP1g, heterochromatin protein 1, gamma; MST, minimal span...