Results 1 - 10 of 25,447

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

by Yongyue Zhang, Michael Brady, Stephen Smith - IEEE TRANSACTIONS ON MEDICAL. IMAGING , 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
Abstract - Cited by 639 (15 self) - Add to MetaCart
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic

Markov Random Field Segmentation of Brain MR Images

by Karsten Held, Elena Rota Kops, Bernd J. Krause, William M. Wells, III, Ron Kikinis, Hans-Wilhelm Müller-Gärtner , 1997
"... We describe a fully-automatic 3Dsegmentation technique for brain MR images. By means of Markov random fields the segmentation algorithm captures three features that are of special importance for MR images: nonparametric distributions of tissue intensities, neighborhood correlations and signal inhomo ..."
Abstract - Cited by 87 (0 self) - Add to MetaCart
We describe a fully-automatic 3Dsegmentation technique for brain MR images. By means of Markov random fields the segmentation algorithm captures three features that are of special importance for MR images: nonparametric distributions of tissue intensities, neighborhood correlations and signal

Hidden Markov Random Field Model and Segmentation of Brain MR Images

by Yongyue Zhang, Stephen Smith, Michael Brady - IEEE TRANSACTIONS ON MEDICAL IMAGING , 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain MR images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain MR images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However,

Intensity standardization simplifies brain MR image segmentation

by Ying Zhuge, Jayaram K. Udupa - Computer Vision and Image Understanding , 2009
"... Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the v ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account

Brain MR Imaging Abnormalities in Kidney Transplant Recipients

by Nada Besenski, Zoran Rumboldt, Osemwegie Emovon, Joyce Nicholas, Sunil Kini, Jovan Milutinovic, Milos N. Budisavljevic
"... BACKGROUND AND PURPOSE: On the basis of limited available data, brain MR imaging abnormalities in kidney transplant recipients (KTRs) have been predominantly attributed to calcineurin inhibitors (CIs), characteristically presenting as posterior reversible encephalop-athy syndrome (PRES). The goal of ..."
Abstract - Add to MetaCart
BACKGROUND AND PURPOSE: On the basis of limited available data, brain MR imaging abnormalities in kidney transplant recipients (KTRs) have been predominantly attributed to calcineurin inhibitors (CIs), characteristically presenting as posterior reversible encephalop-athy syndrome (PRES). The goal

MULTI FRACTAL ANALYSIS OF HUMAN BRAIN MR IMAGE

by Tarun Patil M S, Madhukar Bhat
"... Brain MR Image can detect many abnormalities like tumor, cysts, bleeding, infection etc. Analysis of brain MRI using image processing techniques has been an active research in the field of medical imaging. In this work, it is shown that MR image of brain represent a multi fractal system which is des ..."
Abstract - Add to MetaCart
Brain MR Image can detect many abnormalities like tumor, cysts, bleeding, infection etc. Analysis of brain MRI using image processing techniques has been an active research in the field of medical imaging. In this work, it is shown that MR image of brain represent a multi fractal system which

Feature Based Registration of Brain Mr Image

by R Sobilal Jini , S Robinson Sobitharaj
"... ABSTRACT Medical image processing is a difficult problem. Not only a registration algorithm needs to capture both large and small scale image deformations, it also has to deal with global and local intensity variations. Two main problems occurs during the registration process of non rigid image. Fi ..."
Abstract - Add to MetaCart
efficiently a new feature based registration of non rigid brain MR image using Uniform Pattern of Spherical Region Descriptor is proposed in this paper. The proposed method is based on a new image feature called Uniform Pattern of Spherical Region Descriptor. This uses two features namely Uniform pattern

Fuzzy Features Selection Technique for Brain MR Images

by R. B. Dubey
"... Brain tumors are the second leading cause of cancer deaths in human throughout the world. Therefore accurate diagnosis is important for successful treatment of brain tumor. When modeling a complex, poorly defined problem with hundreds of possible inputs one must identify the significant inputs befor ..."
Abstract - Add to MetaCart
features selection techniques have become an apparent need in many bio-informatics applications such as brain MR images. In order to get best information a new fuzzy feature selection approach for MR brain tumor image have been used for the first time. The proposed method presents the two stages fuzzy

AUTOMATIC VOLUMETRY CAN REVEAL VISUALLY UNDETECTED DISEASE FEATURES ON BRAIN MR IMAGES IN TEMPORAL LOBE EPILEPSY

by Paul Aljabar, Joseph V Hajnal, Daniel Rueckert, See Profile, Alexander Hammers, S. Keihaninejada, R. A. Heckemanna, I. S. Gousiasa, P. Aljabarc , 2010
"... Automatic volumetry can reveal visually undetected disease features on brain MR images in temporal lobe epilepsy. ..."
Abstract - Add to MetaCart
Automatic volumetry can reveal visually undetected disease features on brain MR images in temporal lobe epilepsy.

BRAIN MR IMAGE CLASSIFICATION USING MULTI- SCALE GEOMETRIC ANALYSIS OF RIPPLET

by Sudeb Das, Manish Chowdhury, Malay K. Kundu
"... Abstract—We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an efficient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain M ..."
Abstract - Add to MetaCart
Abstract—We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an efficient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain
Results 1 - 10 of 25,447