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Segregation and integration are two general principles of the brain's functional architecture; therefore brain network analysis is of significant importance in understanding brain function. Critical to brain network analysis and construction is the identification of reliable, reproducible and accurate network nodes, or Regions of Interest (ROIs). In this paper, based on functional ROIs derived from...
We present an algorithmic pipeline to assess the dynamics on human brain networks based on multimodal resting state functional magnetic resonance imaging (rsfMRI) and diffusion tensor imaging (DTI) data. We employ white matter fiber density information to parcellate the cerebral cortex into functionally homogenous regions, which are used as nodes to construct functional brain networks. Then, the dynamics...
In vivo parcellation of the cerebral cortex via non-invasive neuroimaging techniques has been in active research for over a decade. A variety of model-driven or data-driven computational approaches have been proposed to parcellate the cortex. A fundamental issue in these parcellation methodologies is the features or attributes used to define boundaries between cortical regions. This paper proposes...
Structural and functional brain connectivity has been extensively studied via diffusion tensor imaging (DTI) and functional MRI (fMRI) in recent years. An important aspect that has not been adequately addressed before is the connectivity state change in structurally-connected brain regions. In this paper, we present an intuitive approach that extracts feature vectors describing the functional connectivity...
In the brain MR images, the boundary of each encephalic tissue is highly irregular. Traditional 3-D reconstruction algorithms are challenged. Owing to its powerful capacity in solving nonlinearity problems, the sphere-shaped support vector machines (SSSVMs) is applied in the 3-D reconstruction. Selecting parameters for SSSVM and the kernel function, however, is a complicated issue. Appropriate parameters...
In MRI images, the boundary of each encephalic tissue is highly irregular. Sphere-Shaped Support Vector Machine (SSSVM) has the advantage of solving high non-linearity and irregularity problems. In this paper, SSSVM is applied in brain MRI image 3D reconstruction. Selecting parameters for SSSVM, however, is a complicated problem. Appropriate parameters can make the model more flexible and help to...
Resting state fMRI (rsfMRI) has been demonstrated to be an effective modality by which to explore the functional networks of the human brain, as the low-frequency oscillations in rsfMRI time courses between spatially distant brain regions show the evidence of correlated activity patterns in the brain. This paper proposes a novel surface-based data-driven framework to explore these networks through...
It is widely believed that the structural connectivity of a brain region largely determines its function. High resolution Diffusion Tensor Imaging (DTI) is now able to image the axonal fibers in vivo and the DTI tractography result provides rich connectivity information. In this paper, a novel method is proposed to employ fiber density information for automatic cortical parcellation based on the premise...
Cortical folding is an essential geometric characteristic of the human cerebral cortex. The cortical folding pattern conveys important information about brain architecture and function. Cortical thickness is another important morphological feature that reflects the size, density, and arrangement of cells in the cortex. Meanwhile, cortical regions are connected by short-distance or long-distance white...
Since the acquired images are often distorted by noise resulting from various experimental and other sources (e.g., the RF tagging effects) in magnetic resonance imaging (MRI), removing noise is a very important work and at the same time useful information should be well preserved. In this paper, a novel, fast denoising algorithm has been proposed to make tradeoff between reducing noise and enlarging...
The human brain anatomy is extremely variable across individuals in terms of its size, shape, and structure patterning. In this paper, a novel method is proposed for grouping brain MR images into different patterns. This method adopts the affinity propagation methodology to partition a population of brain images into different clusters. In the affinity propagation method, the tissue-segmented and...
Studying the growth/recurrence of glioblastoma multiforme (GBM) is very important not only for diagnosis but also for understanding and detecting the recurrence of GBM after surgery. In this paper, a novel DTI-based method is proposed to analyze the recurrence pattern of GBM based on serial magnetic resonance imaging (MRI). After detecting the tumor shapes from T1-weighted images, the diffusion pattern...
In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. As a new kind of machine learning, support vector machine (SVM) based on statistical learning theory (SLT) has high generalization ability, especially for dataset with small number of samples in high dimensional space. SVM was originally developed...
In head MRI image sequences, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional 3D modeling algorithms. Support vector machine (SVM) based on statistical learning theory has solid theoretical foundation. sphere-shaped SVM (SSSVM) was originally developed for solving some special classification problems. In this paper, it is extended to...
Due to complexity and irregulation of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image has been a hot area. Support vector machine (SVM) based on statistical learning theory is mainly utilized in classification and regression. One Class SVM (OCSVM) was originally proposed for solving some special classification problems. In this paper, OCSVM, which tries to find...
In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. Support vector machine (SVM) has high generalization ability, especially for dataset with small number of samples in high dimensional space. However, selecting parameters for SVM is a complicated problem which directly affects segmentation result...
Registration of DTI data with structure data, such as SPGR data, has import application in quantitative analysis of brain microstructures such as tissue diffusivity. However, due to potential problems such as EPI geometric distortion, partial volume effect and image reslicing errors, accurate registration of these two types of MRI images is challenging. In this paper, we present a novel deformable...
Reconstruction of the geometric central surface of the human cerebral cortex is an important means to study the structure and function of the brain cortex. In this paper, we propose a novel method based on an elastic deformable transform vector field to drive a deformable model for the reconstruction of the central surface of the brain cortex. In addition, simulated brain cortexes are generated to...
Denoising is an important step for image processing. One of the most important characteristics of MRI (MRI) is the complicated changes of gray level. For MRI, preservation of useful information is more important than simple improvement of Signal-Noise Ratio (SNR). Traditional filtering algorithms are not fit for MRI. Adaptive Template Filtering Method (ATFM) can dynamically match the best template...
Intelligent Optimization Algorithm (IOA) mainly includes Immune Algorithm (IA) and Genetic Algorithm (GA). One of the most important characteristics of MRI is the complicated changes of gray level. Traditional filtering algorithms are not fit for MRI. Adaptive Template Filtering Method (ATFM) is an appropriate denoising method for MRI. However, selecting threshold for ATFM is a complicated problem...
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