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41,707
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
- 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
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Cited by 619 (14 self)
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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
Quantum Gravity
, 2004
"... We describe the basic assumptions and key results of loop quantum gravity, which is a background independent approach to quantum gravity. The emphasis is on the basic physical principles and how one deduces predictions from them, at a level suitable for physicists in other areas such as string theor ..."
Abstract
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Cited by 566 (11 self)
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We describe the basic assumptions and key results of loop quantum gravity, which is a background independent approach to quantum gravity. The emphasis is on the basic physical principles and how one deduces predictions from them, at a level suitable for physicists in other areas such as string
Multi-Modal Volume Registration by Maximization of Mutual Information
, 1996
"... A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure, ..."
Abstract
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Cited by 459 (23 self)
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, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with raw images; no preprocessing or feature detection is required. As opposed to feature
Adaptive Segmentation of MRI data
, 1995
"... Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods requi ..."
Abstract
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Cited by 220 (15 self)
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require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the EM algorithm leads to a method that allows for more accurate
MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35: W375–383
, 2007
"... and nucleic acids ..."
adipose tissue: function and physiological significance. Physiol Rev 84
, 2004
"... A. Norepinephrine signaling through �3-receptors leads to thermogenesis 280 B. Thermogenesis is due to activation of UCP1 through lipolysis 283 C. The �2-adrenergic pathway inhibits thermogenesis 288 D. The �1-adrenergic pathway and the cell membrane events 289 III. The Life of the Brown Adipocyte I ..."
Abstract
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Cited by 308 (4 self)
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A. Norepinephrine signaling through �3-receptors leads to thermogenesis 280 B. Thermogenesis is due to activation of UCP1 through lipolysis 283 C. The �2-adrenergic pathway inhibits thermogenesis 288 D. The �1-adrenergic pathway and the cell membrane events 289 III. The Life of the Brown Adipocyte
Improved Localization of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach
- J. Cogn. Neurosci
, 1993
"... We describe a comprehensive linear approach to the prob- lem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole st ..."
Abstract
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Cited by 248 (14 self)
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We describe a comprehensive linear approach to the prob- lem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole
Nonlinear Anisotropic Filtering Of MRI Data
, 1992
"... Despite significant improvements in image quality over the past several years, the full exploitation of magnetic resonance image (MRI) data is often limited by low signal to noise ratio (SNR) or contrast to noise ratio (CNR). In implementing new MR techniques, the criteria of acquisition speed and i ..."
Abstract
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Cited by 192 (15 self)
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Despite significant improvements in image quality over the past several years, the full exploitation of magnetic resonance image (MRI) data is often limited by low signal to noise ratio (SNR) or contrast to noise ratio (CNR). In implementing new MR techniques, the criteria of acquisition speed
Results 1 - 10
of
41,707
