Stripe noise removal in conductive atomic force microscopy.

Conductive atomic force microscopy (c-AFM) can provide simultaneous maps of the topography and electrical current flow through materials with high spatial resolution and it is playing an increasingly important role in the characterization of novel materials that are being investigated for novel memory devices. However, noise in the form of stripe features often appear in c-AFM images, challenging the quantitative analysis of conduction or topographical information. To remove stripe noise without losing interesting information, as many as sixteen destriping methods are investigated in this paper, including three additional models that we propose based on the stripes characteristics, and thirteen state-of-the-art destriping methods. We have also designed a gradient stripe noise model and obtained a ground truth dataset consisting of 800 images, generated by rotating and cropping a clean image, and created a noisy image dataset by adding random intensities of simulated noise to the ground truth dataset. In addition to comparing the results of the stripe noise removal visually, we performed a quantitative image quality comparison using simulated datasets and 100 images with very different strengths of simulated noise. All results show that the Low-Rank Recovery method has the best performance and robustness for removing gradient stripe noise without losing useful information. Furthermore, a detailed performance comparison of Polynomial fitting and Low-Rank Recovery at different levels of real noise is presented.

Distinguishing Patients With a Coordination Disorder From Healthy Controls Using Local Features of Movement Trajectories During the Finger-to-Nose Test.

Assessment of coordination disorders is valuable for monitoring progression of patients, distinguishing healthy and pathological conditions, and ultimately aiding in clinical decision making, thereby offering the possibility to improve medical care or rehabilitation. A common method to assess movement disorders is by using clinical rating scales. However, rating scales depend on the evaluation and interpretation of an observer, implying that subjective phenotypic assignment precedes the application of the scales. Objective and more accurate methods are under continuous development but gold standards are still scarce. Here, we show how a method we previously developed, originally aimed at assessing dynamic balance by a probabilistic generalized linear model, can be used to assess a broader range of functional movements. In this paper, the method is applied to distinguish patients with coordination disorders from healthy controls. We focused on movements recorded during the finger-to-nose task (FNT), which is commonly used to assess coordination disorders. We also compared clinical FNT scores and model scores. Our method achieved 84% classification accuracy in distinguishing patients and healthy participants, using only two features. Future work could entail testing the reliability of the method by using additional features and other clinical tests such as finger chasing, quiet standing, and/or usage of tracking devices such as depth cameras or force plates.

Assessing Dynamic Balance Performance During Exergaming Based on Speed and Curvature of Body Movements.

Improving balance performance among the elderly is of utmost importance because of the increasing number of injuries and fatalities caused by fall incidences. Digital games controlled by body movements (exergames) have been proposed as a way to improve balance among older people. However, the assessment of balance performance in real-time during exergaming remains a challenging task. This assessment could be used to provide instantaneous feedback and automatically adjust the exergame difficulty. Such features could potentially increase the motivation of the player, thus augmenting the effectiveness of exergames. As clear differences in balance performance have been identified between older and younger people, distinguishing between older and younger adults can help identifying measures of balance performance. We used generalized linear models to investigate whether the assessment of balance performance based on movement speed can be improved by incorporating curvature of the movement trajectory into the analysis. Indeed, our results indicated that curvature improves the performance of the models. Five-fold cross validation indicated that our method is promising for the assessment of balance performance in real-time by showing more than 90% classification accuracy. Finally, this method could be valuable not only for exergaming, but also for real-time assessment of body movements in sports, rehabilitation, and medicine.

Data-driven visualization of multichannel EEG coherence networks based on community structure analysis.

An electroencephalography (EEG) coherence network is a representation of functional brain connectivity, and is constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Typical visualizations of coherence networks use a matrix representation with rows and columns representing electrodes and cells representing coherences between electrode signals, or a 2D node-link diagram with vertices representing electrodes and edges representing coherences. However, such representations do not allow an easy embedding of spatial information or they suffer from visual clutter, especially for multichannel EEG coherence networks. In this paper, a new method for data-driven visualization of multichannel EEG coherence networks is proposed to avoid the drawbacks of conventional methods. This method partitions electrodes into dense groups of spatially connected regions. It not only preserves spatial relationships between regions, but also allows an analysis of the functional connectivity within and between brain regions, which could be used to explore the relationship between functional connectivity and underlying brain structures. As an example application, the method is applied to the analysis of multichannel EEG coherence networks obtained from older and younger adults who perform a cognitive task. The proposed method can serve as a preprocessing step before a more detailed analysis of EEG coherence networks.

Exploration of the Brain's White Matter Structure through Visual Abstraction and Multi-Scale Local Fiber Tract Contraction.

We present a visualization technique for brain fiber tracts from DTI data that provides insight into the structure of white matter through visual abstraction. We achieve this abstraction by analyzing the local similarity of tract segment directions at different scales using a stepwise increase of the search range. Next, locally similar tract segments are moved toward each other in an iterative process, resulting in a local contraction of tracts perpendicular to the local tract direction at a given scale. This not only leads to the abstraction of the global structure of the white matter as represented by the tracts, but also creates volumetric voids. This increase of empty space decreases the mutual occlusion of tracts and, consequently, results in a better understanding of the brain's three-dimensional fiber tract structure. Our implementation supports an interactive and continuous transition between the original and the abstracted representations via various scale levels of similarity. We also support the selection of groups of tracts, which are highlighted and rendered with the abstracted visualization as context.

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

BACKGROUND: Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. METHODS: Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. RESULTS: Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%. CONCLUSIONS: Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.

Interactive segmentation and visualization of DTI data using a hierarchical watershed representation.

Magnetic resonance diffusion tensor imaging (DTI) measures diffusion of water molecules and is used to characterize orientation of white matter fibers and connectivity of neurological structures. Segmentation and visualization of DT images is challenging, because of low data quality and complexity of anatomical structures. In this paper, we propose an interactive segmentation approach, based on a hierarchical representation of the input DT image through a tree structure. The tree is obtained by successively merging watershed regions, based on the morphological waterfall approach, hence the name watershed tree. Region merging is done according to a combined similarity and homogeneity criterion. We introduce filters that work on the proposed tree representation, and that enable region-based attribute filtering of DTI data. Linked views between the visualizations of the simplified DT image and the tree enable a user to visually explore both data and tree at interactive rates. The coupling of filtering, semiautomatic segmentation by labeling nodes in the tree, and various interaction mechanisms support the segmentation task. Our method is robust against noise, which we demonstrate on synthetic and real DTI data.

The Alzheimer's disease-related glucose metabolic brain pattern.

PURPOSE: [(18)F]fluorodeoxyglucose (FDG) PET imaging of the brain can be used to assist in the differential diagnosis of dementia. Group differences in glucose uptake between patients with dementia and controls are well-known. However, a multivariate analysis technique called scaled subprofile model, principal component analysis (SSM/PCA) aiming at identifying diagnostic neural networks in diseases, have been applied less frequently. We validated an Alzheimer's Disease-related (AD) glucose metabolic brain pattern using the SSM/PCA analysis and applied it prospectively in an independent confirmation cohort. METHODS: We used FDG-PET scans of 18 healthy controls and 15 AD patients (identification cohort) to identify an AD-related glucose metabolic covariance pattern. In the confirmation cohort (n=15), we investigated the ability to discriminate between probable AD and non-probable AD (possible AD, mild cognitive impairment (MCI) or subjective complaints). RESULTS: The AD-related metabolic covariance pattern was characterized by relatively decreased metabolism in the temporoparietal regions and relatively increased metabolism in the subcortical white matter, cerebellum and sensorimotor cortex. Receiver-operating characteristic (ROC) curves showed at a cut-off value of z=1.23, a sensitivity of 93% and a specificity of 94% for correct AD classification. In the confirmation cohort, subjects with clinically probable AD diagnosis showed a high expression of the AD-related pattern whereas in subjects with a non-probable AD diagnosis a low expression was found. CONCLUSION: The Alzheimer's disease-related cerebral glucose metabolic covariance pattern identified by SSM/PCA analysis was highly sensitive and specific for Alzheimer's disease. This method is expected to be helpful in the early diagnosis of Alzheimer's disease in clinical practice.

Parkinson's disease-related perfusion and glucose metabolic brain patterns identified with PCASL-MRI and FDG-PET imaging.

INTRODUCTION: Under normal conditions, the spatial distribution of resting cerebral blood flow and cerebral metabolic rate of glucose are closely related. A relatively new magnetic resonance (MR) technique, pseudo-continuous arterial spin labeling (PCASL), can be used to measure regional brain perfusion. We identified a Parkinson's disease (PD)-related perfusion and metabolic covariance pattern in the same patients using PCASL and FDG-PET imaging and assessed (dis)similarities in the disease-related pattern between perfusion and metabolism in PD patients. METHODS: Nineteen PD patients and seventeen healthy controls underwent [(18)F]-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging. Of 14 PD patients and all healthy controls PCASL-MRI could be obtained. Data were analyzed using scaled subprofile model/principal component analysis (SSM/PCA). RESULTS: Unique Parkinson's disease-related perfusion and metabolic covariance patterns were identified using PCASL and FDG-PET in the same patients. The PD-related metabolic covariance brain pattern is in high accordance with previously reports. Also our disease-related perfusion pattern is comparable to the earlier described perfusion pattern. The most marked difference between our perfusion and metabolic patterns is the larger perfusion decrease in cortical regions including the insula. CONCLUSION: We identified PD-related perfusion and metabolic brain patterns using PCASL and FDG-PET in the same patients which were comparable with results of existing research. In this respect, PCASL appears to be a promising addition in the early diagnosis of individual parkinsonian patients.

Group-invariant colour morphology based on frames.

Mathematical morphology is a very popular framework for processing binary or grayscale images. One of the key problems in applying this framework to color images is the notorious false color problem. We discuss the nature of this problem and its origins. In doing so, it becomes apparent that the lack of invariance of operators to certain transformations (forming a group) plays an important role. The main culprits are the basic join and meet operations, and the associated lattice structure that forms the theoretical basis for mathematical morphology. We show how a lattice that is not group invariant can be related to another lattice that is. When all transformations in a group are linear, these lattices can be related to one another via the theory of frames. This provides all the machinery to let us transform any (grayscale or color) morphological filter into a group-invariant filter on grayscale or color images. We then demonstrate the potential for both subjective and objective improvement in selected tasks.

Validation of parkinsonian disease-related metabolic brain patterns.

BACKGROUND: The objective of this study was to validate disease-related metabolic brain patterns for Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. METHODS: The study included 20 patients with Parkinson's disease, 21 with multiple system atrophy, and 17 with progressive supranuclear palsy, all of whom had undergone a clinically motivated [18F]-fluoro-deoxyglucose positron emission tomography scan at an early stage of their disease. At a follow-up time after the scan of 2-4 years, a clinical diagnosis was made according to established clinical research criteria. Patient groups were compared with 18 healthy controls using a multivariate covariance image analysis technique called scaled subprofile model/principal component analysis. RESULTS: Disease-related metabolic brain patterns for these parkinsonian disorders were identified. Validation showed that these patterns were highly discriminative of the 3 disorders. CONCLUSIONS: Early diagnosis of parkinsonian disorders is feasible when the expression of disease-related metabolic brain patterns is quantified at a single-subject level.

Fast Sparse Level Sets on Graphics Hardware.

The level-set method is one of the most popular techniques for capturing and tracking deformable interfaces. Although level sets have demonstrated great potential in visualization and computer graphics applications, such as surface editing and physically based modeling, their use for interactive simulations has been limited due to the high computational demands involved. In this paper, we address this computational challenge by leveraging the increased computing power of graphics processors, to achieve fast simulations based on level sets. Our efficient, sparse GPU level-set method is substantially faster than other state-of-the-art, parallel approaches on both CPU and GPU hardware. We further investigate its performance through a method for surface reconstruction, based on GPU level sets. Our novel multiresolution method for surface reconstruction from unorganized point clouds compares favorably with recent, existing techniques and other parallel implementations. Finally, we point out that both level-set computations and rendering of level-set surfaces can be performed at interactive rates, even on large volumetric grids. Therefore, many applications based on level sets can benefit from our sparse level-set method.

Time-resolved transcriptomics and bioinformatic analyses reveal intrinsic stress responses during batch culture of Bacillus subtilis.

We have determined the time-resolved transcriptome of the model gram-positive organism B. subtilis during growth in a batch fermentor on rich medium. DNA microarrays were used to monitor gene transcription using 10-minute intervals at 40 consecutive time points. From the growth curve and analysis of all gene expression levels, we identified 4 distinct growth phases and one clear transition point: a lag phase, an exponential growth phase, the transition point and the very clearly separated early and late stationary growth phases. The gene expression profiles suggest the occurrence of stress responses at specific times although no external stresses were applied. The first one is a small induction of the SigB regulon that occurs at the transition point. Remarkably, a very strong response is observed for the SigW regulon, which is highly upregulated at the onset of the late stationary phase. Bioinformatic analyses that were performed on our data set suggest several novel putative motifs for regulator binding. In addition, the expression profiles of several genes appeared to correlate with the oxygen concentration. This data set of the expression profiles of all B. subtilis genes during the entire growth curve on rich medium constitutes a rich repository that can be further mined by the scientific community.

Heuristics for connectivity-based brain parcellation of SMA/pre-SMA through force-directed graph layout.

We propose the use of force-directed graph layout as an explorative tool for connectivity-based brain parcellation studies. The method can be used as a heuristic to find the number of clusters intrinsically present in the data (if any) and to investigate their organisation. It provides an intuitive representation of the structure of the data and facilitates interactive exploration of properties of single seed voxels as well as relations among (groups of) voxels. We validate the method on synthetic data sets and we investigate the changes in connectivity in the supplementary motor cortex, a brain region whose parcellation has been previously investigated via connectivity studies. This region is supposed to present two easily distinguishable connectivity patterns, putatively denoted by SMA (supplementary motor area) and pre-SMA. Our method provides insights with respect to the connectivity patterns of the premotor cortex. These present a substantial variation among subjects, and their subdivision into two well-separated clusters is not always straightforward.

A diffusion tensor imaging study on the auditory system and tinnitus.

Tinnitus is an auditory percept in the absence of an external sound source. Mechanisms in the central nervous system are believed to be key in the pathophysiology of tinnitus. Diffusion tensor imaging (DTI) is an MR imaging technique that allows in vivo exploration of white matter tissue in the human brain. Using a probabilistic DTI approach, we determined the characteristics of fiber tracts from the inferior colliculus to the medial geniculate body up to the primary auditory cortex. We also investigated the connections between the auditory system and the amygdala, which may be involved in some forms of tinnitus. White matter tracts were characterized by three quantities: the mean fractional anisotropy, the weighted mean fractional anisotropy and the path strength. All these quantities are measures of the patency of white matter tracts. The most important finding is an increased patency of the white matter tracts between the auditory cortex and the amygdala in tinnitus patients as compared to healthy controls.

SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks.

UNLABELLED: SpotXplore is a plugin for Cytoscape for extraction and visualization of differentially expressed subnetworks (hotspots) from gene networks. The hotspot-based visualization approach enables interactive exploration of regulatory interactions in differentially expressed gene sets, and it allows a researcher to explore gene expression in direct relation to the affected cellular gene network. The hotspots provide a view beyond the commonly used metabolic pathways and gene ontologies. AVAILABILITY: http://www.win.tue.nl/∼mwestenb/spotxplore/.

A neurophysiologically plausible population code model for feature integration explains visual crowding.

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

Depth-dependent halos: illustrative rendering of dense line data.

We present a technique for the illustrative rendering of 3D line data at interactive frame rates. We create depth-dependent halos around lines to emphasize tight line bundles while less structured lines are de-emphasized. Moreover, the depth-dependent halos combined with depth cueing via line width attenuation increase depth perception, extending techniques from sparse line rendering to the illustrative visualization of dense line data. We demonstrate how the technique can be used, in particular, for illustrating DTI fiber tracts but also show examples from gas and fluid flow simulations and mathematics as well as describe how the technique extends to point data. We report on an informal evaluation of the illustrative DTI fiber tract visualizations with domain experts in neurosurgery and tractography who commented positively about the results and suggested a number of directions for future work.

A crowding model of visual clutter.

Visual information is difficult to search and interpret when the density of the displayed information is high or the layout is chaotic. Visual information that exhibits such properties is generally referred to as being "cluttered." Clutter should be avoided in information visualizations and interface design in general because it can severely degrade task performance. Although previous studies have identified computable correlates of clutter (such as local feature variance and edge density), understanding of why humans perceive some scenes as being more cluttered than others remains limited. Here, we explore an account of clutter that is inspired by findings from visual perception studies. Specifically, we test the hypothesis that the so-called "crowding" phenomenon is an important constituent of clutter. We constructed an algorithm to predict visual clutter in arbitrary images by estimating the perceptual impairment due to crowding. After verifying that this model can reproduce crowding data we tested whether it can also predict clutter. We found that its predictions correlate well with both subjective clutter assessments and search performance in cluttered scenes. These results suggest that crowding and clutter may indeed be closely related concepts and suggest avenues for further research.

Changes in cortical grey matter density associated with long-standing retinal visual field defects.

Retinal lesions caused by eye diseases such as glaucoma and age-related macular degeneration can, over time, eliminate stimulation of parts of the visual cortex. This could lead to degeneration of inactive cortical neuronal tissue, but this has not been established in humans. Here, we used magnetic resonance imaging to assess the effects of prolonged sensory deprivation in human visual cortex. High-resolution anatomical magnetic resonance images were obtained in subjects with foveal (age-related macular degeneration) and peripheral (glaucoma) retinal lesions as well as age-matched controls. Comparison of grey matter between patient and control groups revealed density reductions in the approximate retinal lesion projection zones in visual cortex. This indicates that long-term cortical deprivation, due to retinal lesions acquired later in life, is associated with retinotopic-specific neuronal degeneration of visual cortex. Such degeneration could interfere with therapeutic strategies such as the future application of artificial retinal implants to overcome lesion-induced visual impairment.