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141
Brain Tissue Classification of Magnetic Resonance Images Using Conditional Random Fields
"... In this project, I propose the application of a discriminative framework for segmentation of a T1weighted magnetic resonance image(MRI). The use of Gaussian mixture models (GMM) is fairly ubiquitous in processing brain images for statistical analysis. This generative framework makes several assumpti ..."
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In this project, I propose the application of a discriminative framework for segmentation of a T1weighted magnetic resonance image(MRI). The use of Gaussian mixture models (GMM) is fairly ubiquitous in processing brain images for statistical analysis. This generative framework makes several
Coil sensitivity encoding for fast MRI. In:
- Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
Abstract
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Cited by 193 (3 self)
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or collimation but by spectral analysis. The idea of Lauterbur (1) to encode object contrast in the resonance spectrum by a magnetic field gradient forms the exclusive basis of signal localization in Fourier imaging. However powerful, the gradient-encoding concept implies a fundamental restriction. Only one
Classification of
"... There are many difficult problems in the field of pattern recognition. These problems are the focus of much active research in order to find efficient approaches to address them. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier ..."
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of the ankle, foot and brain. In proposed methodology three supervised neural networks has been used: Back Propagation Algorithm (BPA), Learning Vector Quantization (LVQ) and Radial Basis Function (RBF). The features of magnetic resonance images have been reduced, using principal component analysis (PCA
Neural Network based Segmentation of Magnetic Resonance Images of the Brain
- IEEE Nuclear Science Symposium & Medical Imaging Conference 3
, 1997
"... This paper presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a Learning Vector Quantization (LVQ) Artificial Neural Network ..."
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Cited by 12 (0 self)
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This paper presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a Learning Vector Quantization (LVQ) Artificial Neural
Experimental Analysis of ANN Based Tissues Segmentation and Classification of Brain MR Images
"... A fully automatic procedure for brain tissue classification of single channel magnetic resonance images (MRI) of human Brain is described. Two different ANN classifiers namely Learning Vector Quantization (LVQ), Multilayer Perceptron (MLP) are used for segmentation (classification) of tissues in bra ..."
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A fully automatic procedure for brain tissue classification of single channel magnetic resonance images (MRI) of human Brain is described. Two different ANN classifiers namely Learning Vector Quantization (LVQ), Multilayer Perceptron (MLP) are used for segmentation (classification) of tissues
Research Article Blind Source Separation of Hemodynamics from Magnetic Resonance Perfusion Brain Images Using Independent Factor Analysis
, 2010
"... Copyright © 2010 Yen-Chun Chou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Perfusion magnetic resonance brain imaging indu ..."
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Copyright © 2010 Yen-Chun Chou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Perfusion magnetic resonance brain imaging
doi:10.1155/2008/780656 Research Article Independent Component Analysis for Magnetic Resonance Image Analysis
"... Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources t ..."
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Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources
Hybridized Classification of Brain MRI using
"... Abstract- Magnetic resonance imaging (MRI) provides detailed anatomic information of any part of the body. In this method a hybrid approach for classification of brain tissue in MRI based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) wavelet based texture feature are extracte ..."
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Abstract- Magnetic resonance imaging (MRI) provides detailed anatomic information of any part of the body. In this method a hybrid approach for classification of brain tissue in MRI based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) wavelet based texture feature
C.: Compare: classification of morphological patterns using adaptive regional elements
- IEEE Transaction on Medical Imaging
, 2007
"... Abstract—This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass ..."
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Cited by 63 (14 self)
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Abstract—This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high
A Weighted K-means Algorithm applied to Brain Tissue Classification
, 2005
"... Tissue classification in Magnetic Resonance (MR) brain images is an important issue in the analysis of several brain dementias. This paper presents a modification of the classical K-means algorithm taking into account the number of times specific features appear in an image, employing, for that purp ..."
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Cited by 5 (0 self)
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Tissue classification in Magnetic Resonance (MR) brain images is an important issue in the analysis of several brain dementias. This paper presents a modification of the classical K-means algorithm taking into account the number of times specific features appear in an image, employing
Results 1 - 10
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141
