AbstractÐThe paper addresses the problem of ªclass-basedº image-based recognition and rendering with varying illumination. The rendering problem is defined as follows: Given a single input image of an object and a sample of images with varying illumination conditions of other objects of the same general class, re-render the input image to simulate new illumination conditions. The class-based recognition problem is similarly defined: Given a single image of an object in a database of images of other objects, some of them are multiply sampled under varying illumination, identify (match) any novel image of that object under varying illumination with the single image of that object in the database. We focus on Lambertian surface classes and, in particular, the class of human faces. The key result in our approach is based on a definition of an illumination invariant signature image which enables an analytic generation of the image space with varying illumination. We show that a small database of objectsÐin our experiments as few as two objectsÐis sufficient for generating the image space with varying illumination of any new object of the class from a single input image of that object. In many cases, the recognition results outperform by far conventional methods and the re-rendering is of remarkable quality considering the size of the database of example images and the mild preprocess required for making the algorithm work.

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays of C. elegans for the study of diverse biological pathways relevant to human disease.

Repetitive mild traumatic brain injury in American football players has garnered increasing public attention following reports of chronic traumatic encephalopathy, a progressive tauopathy. While the mechanisms underlying repetitive mild traumatic brain injury-induced neurodegeneration are unknown and antemortem diagnostic tests are not available, neuropathology studies suggest a pathogenic role for microvascular injury, specifically blood–brain barrier dysfunction. Thus, our main objective was to demonstrate the effectiveness of a modified dynamic contrast-enhanced MRI approach we have developed to detect impairments in brain microvascular function. To this end, we scanned 42 adult male amateur American football players and a control group comprising 27 athletes practicing a non-contact sport and 26 non-athletes. MRI scans were also performed in 51 patients with brain pathologies involving the blood–brain barrier, namely malignant brain tumours, ischaemic stroke and haemorrhagic traumatic contusion. Based on data from prolonged scans, we generated maps that visualized the permeability value for each brain voxel. Our permeability maps revealed an increase in slow blood-to-brain transport in a subset of amateur American football players, but not in sex- and age-matched controls. The increase in permeability was region specific (white matter, midbrain peduncles, red nucleus, temporal cortex) and correlated with changes in white matter, which were confirmed by diffusion tensor imaging. Additionally, increased permeability persisted for months, as seen in players who were scanned both on- and off-season. Examination of patients with brain pathologies revealed that slow tracer accumulation characterizes areas surrounding the core of injury, which frequently shows fast blood-to-brain transport. Next, we verified our method in two rodent models: rats and mice subjected to repeated mild closed-head impact injury, and rats with vascular injury inflicted by photothrombosis. In both models, slow blood-to-brain transport was observed, which correlated with neuropathological changes. Lastly, computational simulations and direct imaging of the transport of Evans blue-albumin complex in brains of rats subjected to recurrent seizures or focal cerebrovascular injury suggest that increased cellular transport underlies the observed slow blood-to-brain transport. Taken together, our findings suggest dynamic contrast-enhanced-MRI can be used to diagnose specific microvascular pathology after traumatic brain injury and other brain pathologies.

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation.

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