Sparse representation of global features of visual images in human primary visual cortex: Evidence from fMRI

In fMRI experiments on object representation in visual cortex, we designed two types of stimuli： one is the gray face image and its line drawing, and the other is the illusion and its corresponding completed illusion. Both of them have the same global features with different minute details so that the results of fMRI experiments can be compared with each other. The first kind of visual stimuli was used in a block design fMRI experiment, and the second was used in an event-related fMRI experiment. Comparing and analyzing interesting visual cortex activity patterns and blood oxygenation level dependent （BOLD）- fMRI signal, we obtained results to show some invariance of global features of visual images. A plausible explanation about the invariant mechanism is related with the cooperation of synchronized response to the global features of the visual image with a feedback of shape perception from higher cortex to cortex V1, namely the integration of global features and embodiment of sparse representation and distributed population code.     

Magnetic resonance imaging based volumetry:a primary approach to unravelling the brain

Magnetic resonance(MR)imaging based volumetry is recognized as an important technique for studying the brain.In this review,two principle volumetric methods using high resolution MR images were introduced,namely the Cavalieri method and the voxel based morphometry(VBM).The Cavalieri method represents a manual technique that allows the volume of brain structures to be estimated efficiently with no systematic error or sampling bias,whereby the VBM represents an automated image analysis which involves the use of statistical parametric mapping of the MR imaging data.Both methods have been refined and applied extensively in recent neuro- science research.The present paper aims to describe the development of methodologies and also to update the knowledge of their applica- tions in studying the normal and diseased brain.     

Right prefrontal laterality of Chinese boys during a Chinese high conflict Stroop task measured by multi-channel near-infrared spectroscopy imaging

Some studies have shown that native Chinese speakers have different laterality in matched Stroop tasks from native English speakers.Recently,many imaging data,which show left laterality of English-matched Stroop interference,have been reported.And a few functional imaging studies have been conducted to investigate the phenomenon of the Chinese version of Stroop task.In this study,functional activity in the lateral prefrontal cortex of a group of normal Chinese boys with functional near-infrared imaging during a Stroop color-word task was measured to show different Stroop interferences in the prefrontal cortex.The results show obvious fluctuation of the cerebral blood volume in the right prefrontal cortex in all boys,which agrees with the finding of previous studies,that is,Chinese native boys have right laterality in their brain when the Chinese version of Stroop color-word task is applied.     

Development of fully 3D image reconstruction techniques for pinhole SPECT imaging

The purpose of this study was to develop fully 3D image reconstruction techniques for pinhole SPECT imaging with our Micro-SPECT system.Our studies involve in the derivation of projection operators,analysis of the sampling characteristics of pinhole SPECT imaging in Radon space,development of effective geometric calibration method for system misalignment,and 3D image reconstruction development and implementation with quantitative degrading compensation for pinhole SPECT with both circular and helical scan.The performances of pinhole SPECT imaging were evaluated using computer simulations and experiments with the Ultra-Micro Hot-Spot phantom,Ultra-Micro Defrise phantom and small-animal imaging.The results from the computer simulations and phantom imaging experiments indicate that the statistically-based iterative algorithms with quantitative compensation provide overall image quality improvement,and the system resolution is significantly recovered for quantitative imaging.The helical pinhole SPECT improves the axial field-of-view (FOV) as compared with the standard pinhole SPECT with circular-orbit scan.The mouse bone imaging experiment shows that the helical pinhole SPECT imaging also provides decent high-resolution whole-body small animal imaging.In conclusion,we have successfully developed a set of valid fully 3D image reconstruction techniques for single-pinhole SPECT imaging.These techniques can be easily extended to multi-pinhole SPECT imaging.     

Finer discrimination of brain activation with local multivariate distance

The organization of human brain function is diverse on different spatial scales. Various cognitive states are always represented as distinct activity patterns across the specific brain region on fine scales. Conventional univariate analysis of functional MRI data seeks to determine how a particular cognitive state is encoded in brain activity by analyzing each voxel separately without considering the fine-scale patterns information contained in the local brain regions. In this paper, a local multivariate distance mapping (LMDM) technique is proposed to detect the brain activation and to map the fine-scale brain activity patterns. LMDM directly represents the local brain activity with the patterns across multiple voxels rather than individual voxels, and it employs the multivariate distance between different patterns to discriminate the brain state on fine scales. Experiments with simulated and real fMRI data demonstrate that LMDM technique can dramatically increase the sensitivity of the detection for the fine-scale brain activity patterns which contain the subtle information of the experimental conditions.     

A binary images watermarking algorithm based on adaptable matrix

A watermarking algorithm of binary images using adaptable matrix is presented. An adaptable matrix is designed to evaluate the smoothness and the connectivity of binary images. The watermark is embedded according to the adaptable matrix in this algorithm. In the proposed watermarking algorithm, each image block implements a XOR operation with the binary adaptable matrix, which has the same size with the image block, and in order to embed the watermark data, a multiplication operation are also implemented with the weight matrix. The experimental results show that proposed scheme has a good performance.     

Image Reconstruction Using a Genetic Algorithm for Electrical Capacitance Tomography

Electrical capacitance tomography （ECT） has been used for more than a decade for imaging dielectric processes. However, because of its ill-posedness and non-linearity, ECT image reconstruction has always been a challenge. A new genetic algorithm （GA） developed for ECT image reconstruction uses initial results from a linear back-projection, which is widely used for ECT image reconstruction to optimize the threshold and the maximum and minimum gray values for the image. The procedure avoids optimizing the gray values pixel by pixel and significantly reduces the search space dimension. Both simulations and static experimental results show that the method is efficient and capable of reconstructing high quality images. Evaluation criteria show that the GA-based method has smaller image error and greater correlation coefficients. In addition, the GA-based method converges quickly with a small number of iterations.     

Image and Video Quality Assessment Using Neural Network and SVM

An image and video quality assessment method was developed using neural network and support vector machines （SVM） with the peak signal to noise ratio （PSNR） and the structure similarity indexes used to describe image quality. The neural network was used to obtain the mapping functions between the objective quality assessment indexes and subjective quality assessment. The SVM was used to classify the images into different types which were accessed using different mapping functions. Video quality was assessed based on the quality of each frame in the video sequence with various weights to describe motion and scene changes in the video. The number of isolated points in the correlations of the image and video subjective and objective quality assessments was reduced by this method. Simulation results show that the method accurately accesses image quality. The monotonicity of the method for images is 6.94% higher than with the PSNR method, and the root mean square error is at least 35.90% higher than with the PSNR.     

Simulation and evaluation of the quality and availability of typical GLI ocean image

ADEOS-II satellite will be launched in the near future. It collocates many remote sensing instruments in the same platform. Among them, Global Image (GLI) is considered to be a main sensor which will play a key role. In order to understand the characteristics of future GLI ocean color images, a simulation and evaluation of the quality and availability of GLI typical ocean image has been done. In the paper, we first introduce the simulation models briefly, and simulate typical cases of radiance images at visible channels, in which the radiance distribution is based on geographic location, the satellite orbital parameters and sensor properties. A method, complex signal noise ratio (CSNR) to evaluate the image quality and availability, is developed according to the characteristics of image. Meanwhile, a series of CSNR images are generated from the simulated radiance components for different cases, which can be used to evaluate the quality and availability of GLI images before the ADEOS-II is placed in orbit. Finally, the quality and availability of GLI images are quantitatively analyzed by the simulated CSNR data. The results will be beneficial to the people who are in charge of GLI mission or plan to use the data from GLI.     

Multifractal analysis of resting state fMRI series in default mode network: age and gender effects

Age-related changes of resting state in default mode network （DMN） may provide new clues to the developing mechanism of normal brain as well as early diagnosis and therapy of some neuropsychiatric disorders. The application of multifractal theory to functional magnetic resonance imaging （fMRI） signals has recently raised increasing attention. We aim to explore the multifractal characteristics underlying the resting state functional magnetic resonance imaging （rs-fMRI） series extracted from DMN, and two issues are mainly discussed： （1） whether there exist multifractals in rs-fMRI series; （2） whether it is possible to distinguish between the different ages or genders by means of multifractal characteristics. Results demon- strated the existence of multifractals in rs-fMRI series in DMN. In addition, slight differences between young subjects and middle-aged or elderly subjects can be successfully detected by △asα, a modified measure we proposed. Furthermore, it is revealed that the rs-fMRI series from young subjects possess smaller averaged scale index and weaker long range correlation, while those from middle-aged or elderly people present increasing averaged scale index andstronger long range correlation. Whereas no significant statistical differences has been found between male and female group. Our results, therefore, highlight the potential useful- ness of multifractal analysis in fMRI series of a certain brain region, and provide important insights into healthy aging in DMN.     

A New Method of Color Edge Detection Based on Local Structure Analysis

Human＇s real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today＇s digital image processing, image segmentation is an important section for computers to ＂understand＂ images and edge detection is always one of the most important methods in the field of image segmentation. Edges in color images are considered as local discontinuities both in color and spatial domains. Despite the intensive study based on integration of single-channel edge detection results, and on vector space analysis, edge detection in color images remains as a challenging issue.     

Estimation of Sediment Incipient Velocity Using B-Mode Ultrasound Imaging Technique

Sediment incipient velocity(SIV) is a vital parameter for sediment research and river dynamics. This paper describes a novel method of estimating SIV based on the known flow velocity in the movable-bed model experiment. In this method, we use B-mode ultrasound imaging technique to get video images of moving particles and topography under water. By statistical analysis of video images, the relationship between the average number of imaging particles and flow velocity is obtained. The relationship between the change rate of average number and flow velocity is analyzed in sediment incipient process. These relationships are used to estimate the SIV. Lastly, the changed topography verifies the estimated velocity. The results show there is a sudden change in these relationships which can be used to estimate the SIV with high resolution by using a B-mode ultrasound device. The estimated SIV of plastic sands(particle size is about 0.25 mm) is 3.64 cm · s–1 and the estimated SIV of natural sands(particle size is about 0.25 mm) is 5.47 cm · s–1 in the same condition.     

New advances in the neural correlates of insight: A decade in review of the insightful brain

The first neuroimaging study of real-time brain activity during insight problem solving was conducted almost ten years ago. Many subsequent studies have used high-resolution event-related potentials (ERPs) and event-related functional magnetic resonance imaging (fMRI) to investigate the temporal dynamics and neural correlates of insight. Recent results on the neural underpinnings of insight have led researchers to propose a neural framework referred to as the "insightful brain". This putative framework represents the neural basis of the cognitive and affective processes that are involved in insight. The insightful brain may involve numerous brain regions, including the lateral prefrontal cortex, cingulate cortex, hippocampus, superior temporal gyrus, fusiform gyrus, precuneus, cuneus, insula and cerebellum. Functional studies have demonstrated that the lateral prefrontal cortex is responsible for mental set shifting and breaking during insight problem solving. The cingulate cortex is involved in the cognitive conflict between new and old ideas and progress monitoring. The hippocampus, superior temporal gyrus and fusiform gyrus form an integrated functional network that specializes in the formation of novel and effective associations. The effective transformation of problem representations depends on a non-verbal visuospatial information-processing network that comprises the precuneus and cuneus. The insula reflects cognitive flexibility and the emotional experience that is associated with insight. The cortical control of finger movements relies on the cerebellum.     

An improved automatic detection method for earthquake-collapsed buildings from ADS40 image

Earthquake-collapsed building identification is important in earthquake damage assessment and is evidence for mapping seismic intensity. After the May 12th Wenchuan major earthquake occurred, experts from CEODE and IPSC collaborated to make a rapid earthquake damage assessment. A crucial task was to identify collapsed buildings from ADS40 images in the earthquake region. The difficulty was to differentiate collapsed buildings from concrete bridges, dry gravels, and landslide-induced rolling stones since they had a similar gray level range in the image. Based on the IPSC method, an improved automatic identification technique was developed and tested in the study area, a portion of Beichuan County. Final results showed that the technique＇s accuracy was over 95%. Procedures and results of this experiment are presented in this article. Theory of this technique indicates that it could be applied to collapsed building identification caused by other disasters.     

Magnetoacoustic tomography with current injection

Magnetoacoustic tomography combines the advantages of electrical impedance tomography with sonography.It can reconstruct conductivity images of biological tissue with high spatial resolution and high contrast.In addition,magnetoacoustic tomography is a new nondestructive functional imaging technology,with which we can realize the early diagnosis of human diseases.In this paper,through theory analysis we deduced the analytical formula of acoustic pressure.Computer simulation and experimental studies on magnetoacoustic tomography with current injection(MAT-CI)were made,and a copper ring was used as the model for the simulation and experiment.Experimental setup and methods are introduced,and the acoustic signals and the image of a metal object by means of MAT-CI are presented.Results of the simulation and experiment were in a good agreement.The reconstructed image of an acoustic source can reflect the conductivity information of a sample.The conductivity distribution of a sample can be obtained by means of MAT-CI.The results provide foundations of study on a model with complex conductivity distribution and tissues for MAT-CI.     

A modified algorithm for SAR parallel imaging

Synthetic aperture radar can provide two dimension images by converting the acquired echoed SAR signal to targets coordinate and reflectivity. With the advancement of sophisticated SAR signal processing, more and more SAR imaging methods have been proposed for synthetic aperture radar which works at near field and the Fresnel approximation is not appropriate. Time domain correlation is a kind of digital reconstruction method based on processing the synthetic aperture radar data in the twodimensional frequency domain via Fourier transform. It reconstructs SAR image via simply correlation without any need for approximation or interpolation. But its high computational cost for correlation makes it unsuitable for real time imaging. In order to reduce the computational burden a modified algorithm about time domain correlation was given in this paper. It also can take full advantage of parallel computations of the imaging processor. Its practical implementation was proposed and the preliminary simulation results were presented. Simulation results show that the proposed algorithm is a computationally efficient way of implementing the reconstruction in real time SAR image processing.     

Resting state brain activity and functional brain mapping

Functional brain imaging studies commonly use either resting or passive task states as their control conditions, and typically identify the activation brain region associated with a specific task by subtracting the resting from the active task conditions. Numerous studies now suggest, however, that the resting state may not reflect true mental “rest” conditions. The mental activity that occurs during “rest” might therefore greatly influence the functional neuroimaging observations that are collected through the usual subtracting analysis strategies. Exploring the ongoing mental processes that occur during resting conditions is thus of particular importance for deciphering functional brain mapping results and obtaining a more comprehensive understanding of human brain functions. In this review article, we will mainly focus on the discussion of the current research background of functional brain mapping at resting state and the physiological significance of the available neuroimaging data.     

Resting state brain activity and functional brain mapping

Functional brain imaging studies commonly use either resting or passive task states as their control conditions, and typically identify the activation brain region associated with a specific task by subtracting the resting from the active task conditions. Numerous studies now suggest, however, that the resting state may not reflect true mental "rest" conditions. The mental activity that occurs during "rest" might therefore greatly influence the functional neuroimaging observations that are collected through the usual subtracting analysis strategies. Exploring the ongoing mental processes that occur during resting conditions is thus of particular importance for deciphering functional brain mapping results and obtaining a more comprehensive understanding of human brain functions. In this review article, we will mainly focus on the discussion of the current research background of functional brain mapping at resting state and the physiological significance of the available neuroimaging data.     

A novel algorithm for fractional resizing of digital image in DCT domain

Fractional resizing of digital images is needed in various applications, such as displaying at different resolution depending on that of display device, building image index for an image database, and changing resolution according to the transmission channel bandwidth. With the wide use of JPEG and MPEG, almost all digital images are stored and transferred in DCT compressed format. In order to save the computation and memory cost, it is desirable to do resizing in DCT domain directly. This paper presents a fast and efficient method, which possesses the capability of fractional resizing in DCT domain. Experimental results confirm that this scheme can achieve significant computation cost reduction while maintain better quality.     

Concept association and hierarchical Hamming clustering model in text classification

We propose two models in this paper. The concept of association model is put forward to obtain the co-occurrence relationships among keywords in the documents and the hierarchical Hamming clustering model is used to reduce the dimensionality of the category feature vector space which can solve the problem of the extremely high dimensionality of the documents~ feature space. The results of experiment indicate that it can obtain the co-occurrence relations among keywords in the documents which promote the recall of classification system effectively. The hierarchical Hamming clustering model can reduce the dimensionality of the category feature vector efficiently, the size of the vector space is only about 10% of the primary dimensionality.