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An accurate registration plays a critical role in group-wise fMRI image analysis. Inspired by the observations that common functional networks can be reconstructed from fMRI image across individuals and in different brain states, we propose a novel computational framework for fMRI image registration by using these common function networks as references for correspondence between individuals. This...
We propose a new reversible image watermarking scheme using adaptive embedding with block varying. Reversible watermarking scheme provides the possibility of recovering original image after watermark extraction process achieve without any distortion. The developed scheme divides an image into blocks of size 2k×2k, and applies 2D-difference expansion method via integer wavelet transform. The watermark...
Multi-agent technology has been considered as an important approach for developing distributed intelligent systems analyzing computed tomography (CT). Due to the important interactions, multi-agent problem complexity can rise rapidly with the number of agents or their behavior. We present a MAS solution that has spawned increasing interest in machine techniques to automate the search and optimization...
Sources of variations in the neural circuitry of the human brain and interrelationship between intrinsic connectivity networks (ICNs) are still a matter of debate and ongoing research. Here, we applied a multivariate analysis of covariance (MANCOVA) based on high-dimensional independent component analysis (ICA) to identify the effects of interoception and related variables on human brain connectome...
Visually lossless irreversible coding permit compression algorithms to improve the compression gain without disturbing the visual image quality. This paper proposes a novel coding scheme in which wavelet based visual model is embedded into lossless compression algorithm to compress the volumetric medical image data. Obtained experimental results are compared with numerically lossless compression schemes...
The resolution of MRI images is limited due to several factors such as imaging hardware or time constraints. However, high MRI image resolution is desired in many medical applications. Traditional Super Resolution (SR) algorithms are generally unable to recover the high frequency (HF) information of MRI images. Recently, spatial adaptive SR algorithms have utilized the combined edge preserving and...
Magnetic induction tomography (MIT) is a non-invasive technology for visualization of the conductivity distribution inside inhomogeneous media. So far, the resolution of MIT has not been high enough for practical applications in biomedical imaging yet. In this research, we investigate the image reconstruction problem using statistical classification method to enhance the resolution of MIT. First,...
Delayed gadolinium enhancement magnetic resonance (DE-MR) imaging can be used for in vivo detection of left atrium (LA) fibrosis and scarring, whose identification is important for atrial fibrillation (AF) treatment. This study presents a new tool for 3D visualization of cardiac LA fibrosis based on DE-MR imaging and its qualitative validation by comparison with electro-anatomic mapping (EAM). Angio-MR...
In the present days, for the human body anatomical study and for the treatment planning medical science very much depend on the medical imaging technology and medical images. Specifically for the human brain, MRI and CT widely prefers and using for the imaging. But by nature medical images are complex and noisy. This leads to the necessity of processes that reduces difficulties in analysis and improves...
Known as “the invisible lesion”, cerebral microinfarcts have been attracting increased attention because of their key role in cognitive decline and dementia. Recently, cerebral microinfarcts have been visualized for the first time in vivo on high resolution 7.0 T MR images. The detection and scoring of microinfarcts requires extensive manual evaluation, is very time-consuming, and highly observer...
This paper proposes a cartilage thickness detection and visualization method that does not utilize a shape model. The proposed method consists of three parts: volume of interest (VOI) initialization, bone segmentation, and cartilage thickness visualization. For VOI initialization, a novel 3D U-shape cuboidal filter is proposed to detect individual bones such as the femur, tibia, and patella, and for...
In the present study, using event-related functional magnetic resonance imaging (fMRI) we measured the tactile memory related brain activations with tactile orientation discrimination task. For each trial, two of three tactile grating domes with same or different orientations (0, 45 and 90°against the proximal-distal line of right index finger) were presented to the subjects' right index fingertip...
This paper describes the efficient framework for the extraction of brain tumor from the MR images. Before the segmentaion process, median filter is used to filter the image, Then, morpholigical gardient is computed and added with the filtered image for the intensity enhancement. After the enhancement process, the thresholding value is calculated using the mean and standard deviation of the image....
In this paper, an efficient technique is proposed for the precise segmentation of normal and pathological tissues in the MRI brain images. The proposed segmentation technique initially performs classification process by utilizing FFBNN. Dual FFBNN networks are used in the classification process. The inputs for these networks are the features that are extracted in two ways from the MRI brain images...
Acupoint specificity, lying at the core of the Traditional Chinese Medicine, still faces many controversies. As previous neuroimaging studies on acupuncture mainly adopted relatively low time-resolution functional magnetic resonance imaging (fMRI) technology and inappropriate block-designed experimental paradigm due to sustained effect, in the current study, we employed a single block-designed paradigm...
Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semiautomatic methods have become the preferred type of medical image segmentation. As Magnetic Resonance Imaging (MRI) is an important technology of...
Cerebral microbleeds have recently received an increased interest, because they appear to be markers of increased risk of vascular events and dementia. Detection and scoring of microbleeds currently requires extensive manual evaluation and hence is very time-consuming. The rating time may be significantly decreased by automated detection of microbleeds using the radial symmetry transform. The goal...
We propose in this paper a Bayesian model for the retrieving of MRI (magnetic resonance imaging) exams that contain cerebral tumors. Bayesian network proved its efficiency and reliability in several AI (Artificial Intelligence) problems and especially in aid-decision applications. To diagnose a cerebral tumor in a MRI exam, we need to interpret diverse sequences and to refer to visual descriptors...
Real time functional magnetic resonance imaging (rtfMRI) allows capturing and analyzing the image of brain activity instantly by measuring the blood oxygen level-dependent (BOLD) signal. Based on rtfMRI, it could provide on-line feedback of human subjects to learn self-regulation of physiological processes, which had shown significant and potential applications in many conducted researches. With the...
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