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The imperfections in the radio-frequency coils or problems associated with the acquisition sequences may cause MRI intensity inhomogeneities, which may mislead image segmentation. Comparing the tradition fuzzy C means model, this paper adds the bias field information in the objective function for simultaneous correction of the bias field and accurately segmentation. In adaptive model, the bias field...
This paper introduces a brain injury detection approach, using 3D filtering technique, for the images acquired by the magnetic resonance imaging (MRI) technique. The proposed method uses the symmetry property of brain MRI on both 2D images and 3D volumetric information of the MRI sequences. The approach consists of two key steps: (1) each slice of a brain image is segmented into different parts using...
Magnetic Resonance Imaging (MRI) provides various imaging modes to study the brain. We tested the benefits of joint analysis of multimodality MRI data using joint independent components analysis (jICA) in comparison to unimodality analyses. Specifically, we designed a jICA to decompose the joint distributions of multimodality MRI data across image voxels and subjects into independent components that...
In this paper, a segmentation technique of multi-spectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Real-coded encoding of the cluster centres is used for this purpose. Here assignments of points to different clusters are made based on the Euclidean distance. The proposed method is applied on several simulated T1-weighted, T2-weighted...
This study presents an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images. In this paper, support vector machine (SVM) based classifier, as a new and powerful kind of supervised machine learning with high generalization characteristics, is employed. Here, least-square SVM (LS-SVM) in conjunction with brain probabilistic atlas as a priori information...
Automatic segmentation of brain tissue on magnetic resonance images is a challenging process due to the variation in brain shapes and similarity of intensity values in the brain and non-brain tissues. Skull stripping is a process of segmenting brain and non-brain tissues in MR brain images. It is an important image processing step in many neuroimage studies. In this paper, we propose a new skull stripping...
This paper presents a high resolution MR image acquisition protocol for better visualization of normal gray structures of brain, a unique feature of the histogram of the reconstructed images of this protocol, an automated segmentation algorithm of the head contour and the entire brain by using this feature. Using signal nulling effect we have emphasized enhancing the intensity difference between white...
Recently introduced in analyzing data from functional MRI (fMRI) and other neuroimaging techniques, Bayesian networks (BN) is a method to characterize effective connectivity patterns among multiple brain regions. So far, interests of using BN have been primarily on learning the connectivity pattern for each single group with well investigated computational algorithms. Examination of the connectivity...
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...
The default-mode network (DMN), which is suggested to have important functions related to internal modes of cognition and increasingly implicated in brain disorders, has attracted much attention in the past few years. Effective connectivity, defined as the influence one neuronal system exerts over another, can provide deep understanding of directed influence between brain regions in the network from...
In this paper we propose a brain extraction method that solely depends on the brain anatomy and its intensity characteristics. Using an adaptive intensity thresholding method on the MRI head scans, a binary image is obtained. The binary image is labeled using the anatomical facts that the scalp is the boundary between head and background, and the skull is the boundary separating brain and scalp. A...
Clustering algorithms have been popularly applied in tissue segmentation in MRI. However, traditional clustering algorithms could not take advantage of some prior knowledge of data even when it does exist. In this paper, we propose a new approach to tissue segmentation of 3D brain MRI using semi-supervised spectral clustering. Spectral clustering algorithm is more powerful than traditional clustering...
The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies; statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. We used a event-related design and contrasted the cognitive processing of Chinese character and figure in left Brodmann areas 44 and 45, which constitute Broca's region. in Chinese-speaking...
This paper describes results for an ongoing research on the segmentation of the posterior fossa and the structures it contains in fetal brain MR images. A semi-automatic segmentation algorithm based on Dijkstra's algorithm and a fast marching level set method is suggested. The algorithm has been tested on a small number of images and compared to manual segmentations done by experts. It shows reasonable...
The purpose of this paper is to investigate the potential and limitations of using multimodal sources of information coming from in vivo NMR and ex vivo NMR data for detecting brain tumors. Supervised pattern recognition methods, whose performance directly depends on the prior available observations used in building them, are proposed. We show that high resolution magic angle spinning (HR-MAS) data...
Spontaneous brain activity studies have revealed `small-world' property in functional networks based on correlated or positively-correlated relationships. However, studies neither investigated negatively-correlated functional networks, nor checked the `dynamic' properties of the whole functional organization. After subjects performed a specific task, what changes will be caused in the intrinsic organization?...
We propose an objective segmentation method for Magnetic Resonance (MR) images of the brain using self-mapping characteristics of one-dimensional Self-Organizing Maps (SOM). The proposed method requires no operators to specify the representative points, but can segment tissues (such as cerebrospinal fluid, gray matter and white matter) needed for diagnosis of brain atrophy. Doing clinical image experiments,...
Registration algorithms can facilitate the automatic anatomical segmentation of pediatric brain MR data sets when segmentation priors (atlases) are in hand. Automatic segmentation can be achieved through label propagation and label fusion in target space. We investigated the performance of different age cohorts used as prior atlases for the segmentation of 13 MRIs of 1-year-olds. Thirty adults and...
A novel MR image acquisition protocol has been presented in order to obtain high resolution image of the brain at acquisition time. For better delineation of the substructures of the brain we have emphasized enhancing the intensity difference between white and gray matters in the reconstructed MR image of the brain. The mathematical basis of the protocol stems from the T1 weighting combined with an...
This paper proposes an empirical study of the efficiency of the Seed-Based Region Growing (SBRG) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this paper, we used controlled experimental data as our testing data...
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