Content-dependent image sampling using mathematical morphology: Application to texture mapping

Interactivity has become one of the main objectives of multimedia applications. Texture mapping techniques are essential to allow its development, but they all have one point in common: they are not content-dependent. In this paper the development of content-dependent downsampling techniques is studied. A general content-dependent sampling process, based on a reference image which indicates the importance given to each pixel of the original image, is proposed. Two methods of building the reference image by means of morphological tools are described. This content-dependent sampling method is used to build a mipmap, a classical structure used in texture mapping.     

基于GPU的工业CT数据场三维纹理映射体绘制算法

本文提出的基于GPU的三维纹理映射算法通过编写顶点程序和片段程序,将传统的基于纹理面片的体绘制算法在GPU中实现.首先将体数据映射为三维纹理并将其载入到显存,接着通过对顶点着色程序和像素着色程序的编写将光线进入点、离开点的计算以及图像的合成运算移入GPU中,最后根据不同的采样点颜色混合公式实现不同的绘制效果.与传统的三...     

Generation and visualization of four-dimensional MR angiography data using an undersampled 3-D projection trajectory

Time-resolved contrast-enhanced magnetic resonance (MR) angiography (CE-MRA) has gained in popularity relative to X-ray Digital Subtraction Angiography because it provides three-dimensional (3-D) spatial resolution and it is less invasive. We have previously presented methods that improve temporal resolution in CE-MRA while providing high spatial resolution by employing an undersampled 3-D projection (3D PR) trajectory. The increased coverage and isotropic resolution of the 3D PR acquisition simplify visualization of the vasculature from any perspective. We present a new algorithm to develop a set of time-resolved 3-D image volumes by preferentially weighting the 3D PR data according to its acquisition time. An iterative algorithm computes a series of density compensation functions for a regridding reconstruction, one for each time frame, that exploit the variable sampling density in 3D PR. The iterative weighting procedure simplifies the calculation of appropriate density compensation for arbitrary sampling patterns, which improve sampling efficiency and, thus, signal-to-noise ratio and contrast-to-noise ratio, since it is does not require a closed-form calculation based on geometry. Current medical workstations can display these large four-dimensional studies, however, interactive cine animation of the data is only possible at significantly degraded resolution. Therefore, we also present a method for interactive visualization using powerful graphics cards and distributed processing. Results from volunteer and patient studies demonstrate the advantages of dynamic imaging with high spatial resolution.     

Correction of motion artifact in cardiac optical mapping using image registration

Cardiac motion is one of the main sources of artifacts in epifluorescence imaging experiments. It can cause significant error in electrophysiological measurements such as action potential duration. We present a novel approach that uses image registration based on maximization of mutual information to correct for in-plane cardiac motion in such experiments. The approach is relatively fast (a few seconds per frame) and is performed entirely post acquisition. The image registration approach is an alternative to traditional approaches such as mechanical restraint of the heart or addition of chemical uncouplers. Our results show that the image registration method significantly reduces motion-related artifacts in experimental data.     

基于IDL的二维海洋遥感数据地图化的实现方法

二维海洋信息对应着物理要素的地理空间分布,通常须叠绘在真实感地图背景上,通过提高图像的可视化显示效果帮助人们理解和分析数据,而如何选择地图图像并使其与待绘制海洋数据有机叠加是海洋信息可视化实现的关键.基于GEBCO数字地图集(GDA)和IDL的对象图形系统,利用累积的阿尔法混合技术,详细阐述了二维海洋数据显示模型的构建与可视化实现途径.目前,该方法已成功用于海洋要素及海洋现象的二维显示.应用事例说明,该方法可有效改善二维海洋信息可视化效果.     

Music via motion: transdomain mapping of motion and sound for interactive performances

This paper presents a framework called Music via Motion (MvM) designed for the transdomain mapping between physical movements of the performer(s) and multimedia events, translating activities from one creative domain to another-for example, from physical gesture to audio output. With a brief background of this domain and prototype designs, the paper describes a number of inter- and multidisciplinary collaborative works for interactive multimedia performances. These include a virtual musical instrument interface, exploring video-based tracking technology to provide an intuitive and nonintrusive musical interface, and sensor-based augmented instrument designs. The paper also describes a distributed multimedia-mapping server which allows multiplatform and multisensory integrations and presents a sample application which integrates a real-time face tracking system. Ongoing developments and plausible future explorations on stage augmentation with virtual and augmented realities as well as gesture analysis on the correlations of musical gesture and physical gesture are also discussed.     

Volume rendering of 3D medical ultrasound data using direct feature mapping

The authors explore the application of volume rendering in medical ultrasonic imaging. Several volume rendering methods have been developed for X-ray computed tomography (X-CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Limited research has been done on applications of volume rendering techniques in medical ultrasound imaging because of a general lack of adequate equipment for 3D acquisitions. Severe noise sources and other limitations in the imaging system make volume rendering of ultrasonic data a challenge compared to rendering of MRI and X-CT data. Rendering algorithms that rely on an initial classification of the data into different tissue categories have been developed for high quality X-CT and MR-data. So far, there is a lack of general and reliable methods for tissue classification in ultrasonic imaging. The authors focus on volume rendering methods which are not dependent on any classification into different tissue categories. Instead, features are extracted from the original 3D data-set, and projected onto the view plane. The authors found that some of these methods may give clinically useful information which is very difficult to get from ordinary 2D ultrasonic images, and in some cases renderings with very fine structural details. The authors have applied the methods to 3D ultrasound images from fetal examinations. The methods are now in use as clinical tools at the National Center of Fetal Medicine in Trondheim, Norway.     

Analysis of fMRI data using improved self-organizing mapping and spatio-temporal metric hierarchical clustering

The self-organizing mapping (SOM) and hierarchical clustering (HC) methods are integrated to detect brain functional activation; functional magnetic resonance imaging (fMRI) data are first processed by SOM to obtain a primary merged neural nodes image, and then by HC to obtain further brain activation patterns. The conventional Euclidean distance metric was replaced by the correlation distance metric in SOM to improve clustering and merging of neural nodes. To improve the use of spatial and temporal information in fMRI data, a new spatial distance (node coordinates in the 2-D lattice) and temporal correlation (correlation degree of each time course in the exemplar matrix) are introduced in HC to merge the primary SOM results. Two simulation studies and two in vivo fMRI data that both contained block-design and event-related experiments revealed that brain functional activation can be effectively detected and that different response patterns can be distinguished using these methods. Our results demonstrate that the improved SOM and HC methods are clearly superior to the statistical parametric mapping (SPM), independent component analysis (ICA), and conventional SOM methods in the block-design, especially in the event-related experiment, as revealed by their performance measured by receiver operating characteristic (ROC) analysis. Our results also suggest that the proposed new integrated approach could be useful in detecting block-design and event-related fMRI data.      

Personalization of cardiac motion and contractility from images using variational data assimilation

Personalization is a key aspect of biophysical models in order to impact clinical practice. In this paper, we propose a personalization method of electromechanical models of the heart from cine-MR images based on the adjoint method. After estimation of electrophysiological parameters, the cardiac motion is estimated based on a proactive electromechanical model. Then cardiac contractilities on two or three regions are estimated by minimizing the discrepancy between measured and simulation motion. Evaluation of the method on three patients with infarcted or dilated myocardium is provided.     

Imaging of cardiac movement using ratiometric and nonratiometric optical mapping: effects of ischemia and 2, 3-butaneodione monoxime

Transmembrane voltage-sensitive fluorescent dyes are used to study electrical activity in hearts. Green and red fluorescence emissions from di-4-ANEPPS excited with 488 nm light indicate both transmembrane voltage changes and heart movement. We have previously shown that the ratio, green fluorescence divided by red fluorescence, indicates the transmembrane voltage without effects of movement. Here we examine the feasibility of measuring the movement, which is useful for the study of cardiac function, by subtracting this ratiometric signal from the red or green fluorescence signal. The results of this subtraction show tissue movement and its relative changes during cardiac ischemia and perfusion with an electromechanical uncoupling agent. By incorporating the spatial variations in fluorescence intensity from the heart, tissue movement can be qualitatively mapped to examine relative changes, however, with limited ability to quantify absolute displacement. Since these maps are obtained simultaneously with corresponding transmembrane potentials, the method allows study of spatiotemporal cardiac movement patterns and their relationship to the action potential.     

Personalization of a cardiac electrophysiology model using optical mapping and MRI for prediction of changes with pacing

Computer models of cardiac electrophysiology (EP) can be a very efficient tool to better understand the mechanisms of arrhythmias. Quantitative adjustment of such models to experimental data (personalization) is needed in order to test their realism and predictive power, but it remains challenging at the organ scale. In this paper, we propose a framework for the personalization of a 3-D cardiac EP model, the Mitchell-Schaeffer (MS) model, and evaluate its volumetric predictive power under various pacing scenarios. The personalization was performed on ex vivo large porcine healthy hearts using diffusion tensor MRI (DT-MRI) and optical mapping data. The MS model was simulated on a 3-D mesh incorporating local fiber orientations, built from DT-MRI. The 3-D model parameters were optimized using features such as 2-D epicardial depolarization and repolarization maps, extracted from the optical mapping. We also evaluated the sensitivity of our personalization framework to different pacing locations and showed results on its robustness. Further, we evaluated volumetric model predictions for various epi- and endocardial pacing scenarios. We demonstrated promising results with a mean personalization error around 5 ms and a mean prediction error around 10 ms (5% of the total depolarization time). Finally, we discussed the potential translation of such work to clinical data and pathological hearts.     

Harnessing the persuasive potential of data: The combinatory effects of data visualization and interactive narratives on obesity perceptions and policy attitudes

Data visualization has become a useful tool for website designers to effectively present complicated statistical information. In the context of health communication, however, the collective nature of aggregated data may hinder users from empathizing with individuals who suffer from a health issue. This study attempts to provide a solution to the limitation, by adding an interactive personal narrative to visualization of obesity data. Given the lack of empirical research that investigates why data visualization influences user engagement, the current study examines interactivity as an underlying mechanism. The level of interactivity in data visualization (low vs. high) was varied, along with the level of interactivity in a narrative of an obese character (low vs. high) in a 2?×?2 factorial-design experiment with 385 valid participants. Results showed that increased message interactivity for the narrative, presented with increased modality interactivity for data visualization, led to a greater empathic perception of obese individuals’ hardships and greater perceived severity of obesity, and less defensive responses to the website. As a result, high interactivity in data visualization showed significant indirect effects on participants’ attitudes toward policy change, only when presented with the highly interactive narrative.     

Solving inverse problems by Bayesian iterative inversion of aforward model with applications to parameter mapping using SMMR remotesensing data

Inverse problems have been often considered ill-posed, i.e., the statement of the problem does not thoroughly constrain the solution space. In this paper the authors take advantage of this lack of information by adding additional informative constraints to the problem solution using Bayesian methodology. Bayesian modeling gains much of its power from its ability to isolate and incorporate causal models as conditional probabilities. As causal models are accurately represented by forward models, the authors convert implicit functional models into data driven forward models represented by neural networks, to be used as engines in a Bayesian modeling setting. Remote sensing problems afford opportunities for inclusion of ground truth information, prior probabilities, noise distributions, and other informative constraints within a Bayesian probabilistic framework. They first apply these Bayesian methods to a synthetic remote sensing problem, showing that the performance is superior to a previously published method of iterative inversion of neural networks. Next, microwave brightness temperatures obtained from the Scanning Multichannel Microwave Radiometer (SMMR) over the African continent are inverted. The values of soil moisture, surface air temperature and vegetation moisture retrieved from the inversion produced contours that agree with the expected trends for that region     

Automatic detection of abnormalities in chest radiographs using local texture analysis

A fully automatic method is presented to detect abnormalities in frontal chest radiographs which are aggregated into an overall abnormality score. The method is aimed at finding abnormal signs of a diffuse textural nature, such as they are encountered in mass chest screening against tuberculosis (TB). The scheme starts with automatic segmentation of the lung fields, using active shape models. The segmentation is used to subdivide the lung fields into overlapping regions of various sizes. Texture features are extracted from each region, using the moments of responses to a multiscale filter bank. Additional "difference features" are obtained by subtracting feature vectors from corresponding regions in the left and right lung fields. A separate training set is constructed for each region. All regions are classified by voting among the k nearest neighbors, with leave-one-out. Next, the classification results of each region are combined, using a weighted multiplier in which regions with higher classification reliability weigh more heavily. This produces an abnormality score for each image. The method is evaluated on two databases. The first database was collected from a TB mass chest screening program, from which 147 images with textural abnormalities and 241 normal images were selected. Although this database contains many subtle abnormalities, the classification has a sensitivity of 0.86 at a specificity of 0.50 and an area under the receiver operating characteristic (ROC) curve of 0.820. The second database consist of 100 normal images and 100 abnormal images with interstitial disease. For this database, the results were a sensitivity of 0.97 at a specificity of 0.90 and an area under the ROC curve of 0.986.     

Active appearance models using statistical characteristics of Gabor based texture representation

Active appearance model (AAM) has been successfully applied to register many types of deformable objects in images. However, the high dimension of intensity used in AAM usually leads to an expensive storage and computational cost. Moreover, intensity values cannot provide enough information for image alignment. In this paper, we propose a new AAM method based on Gabor texture feature representation. Our contributions are two-fold. On one hand, based on the assumption that Gabor magnitude and Gabor phase follow a lognormal distribution and a general Gaussian distribution respectively, three simplified texture representations are proposed. One the other hand, we apply the proposed texture representations in AAM, which is the first time to extract statistical features from both Gabor magnitude and Gabor phase as the texture representation in AAM. Tests on public and our databases show that the proposed Gabor representations lead to more accurate and robust matching between model and images.     

A novel approach to dual excitation ratiometric optical mapping of cardiac action potentials with di-4-ANEPPS using pulsed LED excitation

We developed a new method for ratiometric optical mapping of transmembrane potential (V(m)) in cardiac preparations stained with di-4-ANEPPS. V(m)-dependent shifts of excitation and emission spectra establish two excitation bands (<481 and >481 nm) that produce fluorescence changes of opposite polarity within a single emission band (575-620 nm). The ratio of these positive and negative fluorescence signals (excitation ratiometry) increases V(m) sensitivity and removes artifacts common to both signals. We pulsed blue (450 ± 10 nm) and cyan (505 ± 15 nm) light emitting diodes (LEDs) at 375 Hz in alternating phase synchronized to a camera (750 frames-per-second). Fluorescence was bandpass filtered (585 ± 20 nm). This produced signals with upright (blue) and inverted (cyan) action potentials (APs) interleaved in sequential frames. In four whole swine hearts with motion chemically arrested, fractional fluorescence for blue, cyan, and ratio signals was 1.2 ± 0.3%, 1.2 ± 0.3%, and 2.4 ± 0.6%, respectively. Signal-to-noise ratios were 4.3 ± 1.4, 4.0 ± 1.2, and 5.8 ± 1.9, respectively. After washing out the electromechanical uncoupling agent, we characterized motion artifact by cross-correlating blue, cyan, and ratio signals with a signal with normal AP morphology. Ratiometry improved cross-correlation coefficients from 0.50 ± 0.48 to 0.81 ± 0.25, but did not cancel all motion artifacts. These findings demonstrate the feasibility of pulsed LED excitation ratiometry in myocardium.     

Venous catheter based mapping of ectopic epicardial activation: training data set selection for statistical estimation

A source of error in most of the existing catheter cardiac mapping approaches is that they are not capable of acquiring epicardial potentials even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this subgroup of patients, mapping techniques that are limited to the endocardium result in localization errors and failure in subsequent ablation procedures. In addition, catheter-based electrophysiological studies of the epicardium are limited to regions near the coronary vessels or require transthoracic access. We have developed a statistical approach by which to estimate high-resolution maps of epicardial activation from very low-resolution multi-electrode venous catheter measurements. A training set of previously recorded maps is necessary for this technique so that composition of the database becomes an important determinant of accuracy. The specific hypothesis of the study was that estimation accuracy would be best when the training data set matches that of the test beat(s), whereby the matching was according to the site of initiation of the beats. This hypothesis suggests approaches to optimized selection of the training set, three of which we have developed and evaluated. One of these methods, the high-CC refinement method, was able to estimate the earliest activation site of left ventricularly paced maps within an average of 4.67 mm of the true site; in 89% of the cases (a total of 231 cases) the error was smaller than 10 mm. In another method, MHC-Spatial activation, right ventricularly paced maps (239 maps) were estimated with an error of 7.15 mm. The average correlation coefficient between the original and the estimated maps was also very high (0.97), which shows the ability of the training data set refinement methods to estimate the epicardial activation sequence. The results of these tests support the hypothesis and, moreover, suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting.     

Classification of cardiac arrhythmias using fuzzy ARTMAP

The authors have investigated the QRS complex, extracted from electrocardiogram (EGG) data, using fuzzy adaptive resonance theory mapping (ARTMAP) to classify cardiac arrhythmias. Two different conditions have been analyzed: normal and abnormal premature ventricular contraction (PVC). Based on MIT/BIH database annotations, cardiac beats for normal and abnormal QRS complexes were extracted from this database, scaled, and Hamming windowed, after bandpass filtering, to yield a sequence of 100 samples for each QRS segment. From each of these sequences, two linear predictive coding (LPC) coefficients were generated using Burg's maximum entropy method. The two LPC coefficients, along with the mean-square value of the QRS complex segment, were utilized as features for each condition to train and test a fuzzy ARTMAP neural network for classification of normal and abnormal PVC conditions. The test results show that the fuzzy ARTMAP neural network can classify cardiac arrhythmias with greater than 99% specificity and 97% sensitivity