Population Level Inference for Multivariate MEG Analysis

Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are those features that maximise these differences. Here we propose a solution based on Canonical Variates Analysis (CVA) model scoring at the subject level and random effects Bayesian model selection at the group level. We apply this approach to beamformer reconstructed MEG data in source space. CVA estimates those multivariate patterns of activation that correlate most highly with the experimental design; the order of a CVA model is then determined by the number of significant canonical vectors. Random effects Bayesian model comparison then provides machinery for inferring the optimal order over the group of subjects. Absence of a multivariate dependence is indicated by the null model being the most likely. This approach can also be applied to CVA models with a fixed number of canonical vectors but supplied with different feature sets. We illustrate the method by identifying feature sets based on variable-dimension MEG power spectra in the primary visual cortex and fusiform gyrus that are maximally discriminative of data epochs before versus after visual stimulation.     

MEG Source Localization Using Invariance of Noise Space

We propose INvariance of Noise (INN) space as a novel method for source localization of magnetoencephalography (MEG) data. The method is based on the fact that modulations of source strengths across time change the energy in signal subspace but leave the noise subspace invariant. We compare INN with classical MUSIC, RAP-MUSIC, and beamformer approaches using simulated data while varying signal-to-noise ratios as well as distance and temporal correlation between two sources. We also demonstrate the utility of INN with actual auditory evoked MEG responses in eight subjects. In all cases, INN performed well, especially when the sources were closely spaced, highly correlated, or one source was considerably stronger than the other.     

Accurate Construction of Photoactivated Localization Microscopy (PALM) Images for Quantitative Measurements

Localization-based superresolution microscopy techniques such as Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) have allowed investigations of cellular structures with unprecedented optical resolutions. One major obstacle to interpreting superresolution images, however, is the overcounting of molecule numbers caused by fluorophore photoblinking. Using both experimental and simulated images, we determined the effects of photoblinking on the accurate reconstruction of superresolution images and on quantitative measurements of structural dimension and molecule density made from those images. We found that structural dimension and relative density measurements can be made reliably from images that contain photoblinking-related overcounting, but accurate absolute density measurements, and consequently faithful representations of molecule counts and positions in cellular structures, require the application of a clustering algorithm to group localizations that originate from the same molecule. We analyzed how applying a simple algorithm with different clustering thresholds (t_Thresh and d_Thresh) affects the accuracy of reconstructed images, and developed an easy method to select optimal thresholds. We also identified an empirical criterion to evaluate whether an imaging condition is appropriate for accurate superresolution image reconstruction with the clustering algorithm. Both the threshold selection method and imaging condition criterion are easy to implement within existing PALM clustering algorithms and experimental conditions. The main advantage of our method is that it generates a superresolution image and molecule position list that faithfully represents molecule counts and positions within a cellular structure, rather than only summarizing structural properties into ensemble parameters. This feature makes it particularly useful for cellular structures of heterogeneous densities and irregular geometries, and allows a variety of quantitative measurements tailored to specific needs of different biological systems.     

Segmentation of center brains and optic lobes in 3D confocal images of adult fruit fly brains

Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial.We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve’s smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties.     

Fast processing of microscopic images using object-based extended depth of field

Background

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue.

Methods

The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time.

Results

We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time.

Conclusions

This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.

     

Nonrigid Registration Regularized by Shape Information: Application to Atlas Construction of Cardiac CT Images

Cardiac atlases play an important role in the computer-aided diagnosis of cardiovascular diseases, in particular they need to deal with large and highly variable image datasets. In this paper, we propose a new nonrigid registration algorithm incorporating shape information, to produce comprehensive atlases. For one thing, the multiscale gradient orientation features of images are combined to form the construction of multifeature mutual information. Additionally, the shape information of multiple-objects in images is incorporated into the cost function for registration. We demonstrate the merits of the new registration algorithm on the 3D data sets of 15 patients. The experimental results show that the new registration algorithm can outperform the conventional intensity-based registration method. The obtained atlas can represent the cardiac structures more accurately.     

Reconstruction of low-resolution molecular structures from simulated atomic force microscopy images

BackgroundAtomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial.MethodWe aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image.ResultsThe algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations.ConclusionsThe proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail.General significanceHigh-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule.     

Application of novel lossless compression of medical images using prediction and contextual error modeling

Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. We show that this approach is well suited for medical image data compression. Results obtained with the proposed compression method on medical images are very encouraging, beating several well known lossless compression methods. The predictor proposed can also be used in other image processing applications such as segmentation and extraction of image regions.     

A New Method for the Analysis of Soft Tissues with Data Acquired under Field Conditions

Analyzing soft-tissue structures is particularly challenging due to the lack of homologous landmarks that can be reliably identified across time and specimens. This is particularly true when data are to be collected under field conditions. Here, we present a method that combines photogrammetric techniques and geometric morphometrics methods (GMM) to quantify soft tissues for their subsequent volumetric analysis. We combine previously developed methods for landmark data acquisition and processing with a custom program for volumetric computations. Photogrammetric methods are a particularly powerful tool for field studies as they allow for image acquisition with minimal equipment requirements and for the acquisition of the spatial coordinates of points (anatomical landmarks or others) from these images. For our method, a limited number of homologous landmarks, i.e., points that can be found on any specimen independent of space and time, and further distinctive points, which may vary over time, space and subject, are identified on two-dimensional photographs and their three-dimensional coordinates estimated using photogrammetric methods. The three-dimensional configurations are oriented by the spatial principal components (PCs) of the homologous points. Crucially, this last step orients the configuration such that x and y-information (PC1 and PC2 coordinates) constitute an anatomically-defined plane with the z-values (PC3 coordinate) in the direction of interest for volume computation. The z-coordinates are then used to estimate the volume of the tissue. We validate our method using a physical, geometric model of known dimensions and physical (wax) models designed to approximate perineal swellings in female macaques. To demonstrate the usefulness and potential of our method, we use it to estimate the volumes of Barbary macaque sexual swellings recorded in the field with video images. By analyzing both the artificial data and real monkey swellings, we validate our method''s accuracy and illustrate its potential for application in important areas of biological research.     

A Practical Approach Based on Analytic Deformable Algorithm for Scenic Image Registration

Background

Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects.

Methods and Findings

An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM) method. It converges evidently faster than most other methods because of its feature-based characteristic.

Conclusions

We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC) and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images.     

Population-level variability in the social climates of four chimpanzee societies

Recent debates have questioned the extent to which culturally-transmitted norms drive behavioral variation in resource sharing across human populations. We shed new light on this discussion by examining the group-level variation in the social dynamics and resource sharing of chimpanzees, a species that is highly social and forms long-term community associations but differs from humans in the extent to which cultural norms are adopted and enforced. We rely on theory developed in primate socioecology to guide our investigation in four neighboring chimpanzee groups at a sanctuary in Zambia. We used a combination of experimental and observational approaches to assess the distribution of resource holding potential in each group. In the first assessment, we measured the proportion of the population that gathered in a resource-rich zone, in the second we assessed naturally occurring social spacing via social network analysis, and in the third we assessed the degree to which benefits were equally distributed within the group. We report significant, stable group-level variation across these multiple measures, indicating that group-level variation in resource sharing and social tolerance is not necessarily reliant upon human-like cultural norms.     

Iterative image reconstruction using modified non-local means filtering for limited-angle computed tomography

PurposeLimited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed.MethodsSince the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV.ResultsSimulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm.ConclusionThis paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods.     

Subject-specific responses to an adaptive ankle prosthesis during incline walking

Individuals with lower-limb amputation often have difficulty walking on slopes, in part due to limitations of conventional prosthetic feet. Conventional prostheses have fixed ankle set-point angles and cannot fully replicate able-bodied ankle dynamics. Microprocessor-controlled ankles have been developed to help overcome these limitations. The objective of this study was to characterize how the slope adaptation feature of a microprocessor-controlled ankle affected individual prosthesis user gait biomechanics during sloped walking. Previous studies on similar microprocessor-controlled ankles have focused on group-level results (inter-subject mean), but did not report individual subject results. Our study builds upon prior work and provides new insight by presenting subject-specific results and investigating to what extent individual responses agree with the group-level results. We performed gait analysis on seven individuals with unilateral transtibial amputation while they walked on a 7.5° incline with a recently redesigned microprocessor-controlled ankle that adjusts ankle set-point angle to the slope. We computed gait kinematics and kinetics, and compared how users walked with vs. without this set-point adjustment. The microprocessor-controlled ankle increased minimum toe clearance for all subjects. Despite the microprocessor-controlled ankle behaving similarly for each user, we observed marked differences in individual responses. For instance, two users switched from a forefoot landing pattern with the microprocessor-controlled ankle locked at neutral angle to rearfoot landing when the microprocessor-controlled ankle adapted to the slope, while two maintained a forefoot and three maintained a rearfoot landing pattern across conditions. Changes in knee angle and moment were also subject-specific. Individual user responses were often not well represented by inter-subject mean. Although the prevailing experimental paradigm in prosthetic gait analysis studies is to focus on group-level analysis, our findings call attention to the high inter-subject variability which may necessitate alternative experimental approaches to assess prosthetic interventions.     

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images.The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models.This new function is a combination of the gray-level information and first-order statistical features,called standard deviation parameters.In a comprehensive study,the developed algorithm and the efficiency of segmentation were first tested for synthetic images.Tests were also performed on breast and liver ultrasound images.The proposed method was compared with the watershed approach to show its efficiency.The performance of the segmentation was estimated using the area error rate.Using the standard deviation textural feature and a 5×5 kernel,our curve evolution was able to produce results close to the minimal area error rate(namely 8.88% for breast images and 10.82% for liver images).The image resolution was evaluated using the contrast-to-gradient method.The experiments showed promising segmentation results.     

Non-uniform strain distribution within rat cartilaginous growth plate under uniaxial compression

Growth plates are highly inhomogeneous in morphology and composition. Mechanical loading can modulate longitudinal bone growth, though the mechanisms underlying this mechanobiology are poorly understood. The proximal tibial growth plates of six rats were tested in vitro under uniaxial compression to 5% strain, and confocal microscopy was used to track and capture images of fluorescently labeled cell nuclei with increasing applied strains. The local strain patterns through the growth plate thickness were quantified using texture correlation analysis. The technique of texture correlation analysis was first validated by comparing theoretical simulated strain maps generated from numerically distorted images. The texture correlation algorithm was sensitive to the grid size superimposed on the original image, but remained insensitive to parameters related to the size of the final image mask, which was searched by the correlation algorithm for each grid point of the original image. Within the growth plate, experimental strain distributions were non-uniform in all six specimens. Growth plates were mostly under compression strains. The strain distributions differed among the histomorphological zones of the growth plate, which was most obvious in specimens with regular growth plate shape: higher compressive strains (4-8 times higher than the applied 5% strain) were located mainly in regions overlapping the reserve and hypertrophic zones with lower compressive strains in the proliferative zone. This study documents the non-uniform mechanical behavior of growth plate across its three histological zones when exposed to compression. Further investigation is required to establish the significance of non-uniform strain fields during growth in vivo.     

基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘

提出一种基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘的算法。该算法在Chan—Vese（CV）模型基础上,定义了一个局部调整项,采用基于水平集的动态轮廓模型提取超声图像乳腺肿瘤边缘。将该算法应用于89例临床超声图像乳腺肿瘤的边缘提取实验,结果表明：该算法比CV模型更适用于具有区域非同质性的超声图像的分割,可有效实现超声图像乳腺肿瘤边缘的提取。     

Bone-induced streak artifact suppression in sparse-view CT image reconstruction

ABSTRACT: BACKGROUND: In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced streak artifact reduction method in the CS-based image reconstruction. METHODS: We firstly identify the high-intensity bony regions from the image reconstructed by the filtered backprojection (FBP) method, and we calculate the sinogram stemming from the bony regions only. Then, we subtract the calculated sinogram, which stands for the bony regions, from the measured sinogram before performing the CS-based image reconstruction. The image reconstructed from the subtracted sinogram will stand for the soft tissues with little streak artifacts on it. To restore the original image intensity in the bony regions, we add the bony region image, which has been identified from the FBP image, to the soft tissue image to form a combined image. Then, we perform the CS-based image reconstruction again on the measured sinogram using the combined image as the initial condition of the iteration. For experimental validation of the proposed method, we take images of a contrast phantom and a rat using a micro-CT and we evaluate the reconstructed images based on two figures of merit, relative mean square error and total variation caused by the streak artifacts. RESULTS: The images reconstructed by the proposed method have been found to have smaller streak artifacts than the ones reconstructed by the original CS-based method when visually inspected. The quantitative image evaluation studies have also shown that the proposed method outperforms the conventional CS-based method. CONCLUSIONS: The proposed method can effectively suppress streak artifacts stemming from bony structures in sparse-view CT imaging.     

Divergent Cortical Generators of MEG and EEG during Human Sleep Spindles Suggested by Distributed Source Modeling

Background

Sleep spindles are ∼1-second bursts of 10–15 Hz activity, occurring during normal stage 2 sleep. In animals, sleep spindles can be synchronous across multiple cortical and thalamic locations, suggesting a distributed stable phase-locked generating system. The high synchrony of spindles across scalp EEG sites suggests that this may also be true in humans. However, prior MEG studies suggest multiple and varying generators.

Methodology/Principal Findings

We recorded 306 channels of MEG simultaneously with 60 channels of EEG during naturally occurring spindles of stage 2 sleep in 7 healthy subjects. High-resolution structural MRI was obtained in each subject, to define the shells for a boundary element forward solution and to reconstruct the cortex providing the solution space for a noise-normalized minimum norm source estimation procedure. Integrated across the entire duration of all spindles, sources estimated from EEG and MEG are similar, diffuse and widespread, including all lobes from both hemispheres. However, the locations, phase and amplitude of sources simultaneously estimated from MEG versus EEG are highly distinct during the same spindles. Specifically, the sources estimated from EEG are highly synchronous across the cortex, whereas those from MEG rapidly shift in phase, hemisphere, and the location within the hemisphere.

Conclusions/Significance

The heterogeneity of MEG sources implies that multiple generators are active during human sleep spindles. If the source modeling is correct, then EEG spindles are generated by a different, diffusely synchronous system. Animal studies have identified two thalamo-cortical systems, core and matrix, that produce focal or diffuse activation and thus could underlie MEG and EEG spindles, respectively. Alternatively, EEG spindles could reflect overlap at the sensors of the same sources as are seen from the MEG. Although our results generally match human intracranial recordings, additional improvements are possible and simultaneous intra- and extra-cranial measures are needed to test their accuracy.     

High-definition mapping of neural activity using voltage-sensitive dyes

The distribution of patterns of activity in different brain structures has been related to the encoding and processing of sensory information. Consequently, it is important to be able to image the distribution of these patterns to understand basic brain functions. The spatial resolution of voltage-sensitive dye (VSD) methods has recently been enhanced considerably by the use of video imaging techniques. The main factor that now hampers the resolution of VSD patterns is the inherent limitation of the optical systems. Unfortunately, the intrinsic characteristics of VSD images impose important limitations that restrict the use of general deconvolution techniques. To overcomes this problem, in this study an image restoration procedure has been implemented that takes into consideration the limiting characteristics of VSD signals. This technique is based on applying a set of imaging processing steps. First, the signal-to-noise (S/N) ratio of the images was improved to avoid an increase in the noise levels during the deconvolution procedures. For this purpose, a new filter technique was implemented that yielded better results than other methods currently used in optical imaging. Second, focal plane images were deconvolved using a modification of the well-known nearest-neighbor deconvolution algorithm. But to reduce the light exposure of the preparation and simplify image acquisition procedures, adjacent image planes were modeled according to the in-focus image planes and the empirical point spread function (PSF) profiles. Third, resulting focal plane responses were processed to reduce the contribution of optical responses that originate in distant image planes. This method was found to be satisfactory under simulated and real experimental conditions. By comparing the restored and unprocessed images, it was clearly demonstrated that this method can effectively remove the out-of-focus artifacts and produce focal plane images of better quality. Evaluations of the tissue optical properties allowed assessment of the maximum practical optical section thickness using this deconvolution technique in the optical system tested. Determination of the three-dimensional PSF permitted the correct application of deconvolution algorithms and the removal of the contaminating light arising from adjacent as well as distant optical planes. The implementation of this deconvolution approach in salamander olfactory bulb allowed the detailed study of the laminar distribution of voltage-sensitive changes across the bulb layer. It is concluded that (1) this deconvolution procedure is well suited to deconvolved low-contrast images and offers important advantages over other alternatives; (2) this method can be properly used only when the tissue optical properties are first determined; (3) high levels of light scattering in the tissue reduce the optical section capabilities of this technique as well as other deconvolution procedures; and (4) use of the highest numerical aperture in the objectives is advisable because this improves not only the light-collecting efficiency to detect poor-contrast images, but also the spatial frequency differences between adjacent image planes. Under this condition it is possible to overcome some of the limitations imposed by the light scattering/birefringence of the tissue.