Literature DB >> 3964938

Multispectral analysis of magnetic resonance images.

M W Vannier, R L Butterfield, D Jordan, W A Murphy, R G Levitt, M Gado.   

Abstract

Magnetic resonance (MR) imaging systems produce spatial distribution estimates of proton density, relaxation time, and flow, in a two dimensional matrix form that is analogous to that of the image data obtained from multispectral imaging satellites. Advanced NASA satellite image processing offers sophisticated multispectral analysis of MR images. Spin echo and inversion recovery pulse sequence images were entered in a digital format compatible with satellite images and accurately registered pixel by pixel. Signatures of each tissue class were automatically determined using both supervised and unsupervised classification. Overall tissue classification was obtained in the form of a theme map. In MR images of the brain, for example, the classes included CSF, gray matter, white matter, subcutaneous fat, muscle, and bone. These methods provide an efficient means of identifying subtle relationships in a multi-image MR study.

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Year:  1985        PMID: 3964938     DOI: 10.1148/radiology.154.1.3964938

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  25 in total

1.  Automated brain tissue segmentation based on fractional signal mapping from inversion recovery Look-Locker acquisition.

Authors:  Wanyong Shin; Xiujuan Geng; Hong Gu; Wang Zhan; Qihong Zou; Yihong Yang
Journal:  Neuroimage       Date:  2010-05-07       Impact factor: 6.556

2.  Novel whole brain segmentation and volume estimation using quantitative MRI.

Authors:  J West; J B M Warntjes; P Lundberg
Journal:  Eur Radiol       Date:  2011-11-24       Impact factor: 5.315

3.  Decomposing the Hounsfield unit: probabilistic segmentation of brain tissue in computed tomography.

Authors:  A Kemmling; H Wersching; K Berger; S Knecht; C Groden; I Nölte
Journal:  Clin Neuroradiol       Date:  2012-01-21       Impact factor: 3.649

4.  Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation.

Authors:  Alireza Akhbardeh; Michael A Jacobs
Journal:  Med Phys       Date:  2012-04       Impact factor: 4.071

5.  Learning likelihoods for labeling (L3): a general multi-classifier segmentation algorithm.

Authors:  Neil I Weisenfeld; Simon K Warfield
Journal:  Med Image Comput Comput Assist Interv       Date:  2011

6.  Partial volume segmentation of brain magnetic resonance images based on maximum a posteriori probability.

Authors:  Xiang Li; Lihong Li; Hongbing Lu; Zhengrong Liang
Journal:  Med Phys       Date:  2005-07       Impact factor: 4.071

7.  Evaluation of automated brain MR image segmentation and volumetry methods.

Authors:  Frederick Klauschen; Aaron Goldman; Vincent Barra; Andreas Meyer-Lindenberg; Arvid Lundervold
Journal:  Hum Brain Mapp       Date:  2009-04       Impact factor: 5.038

8.  An open source multivariate framework for n-tissue segmentation with evaluation on public data.

Authors:  Brian B Avants; Nicholas J Tustison; Jue Wu; Philip A Cook; James C Gee
Journal:  Neuroinformatics       Date:  2011-12

Review 9.  Three-dimensional imaging in craniofacial surgery.

Authors:  F W Zonneveld; S Lobregt; J C van der Meulen; J M Vaandrager
Journal:  World J Surg       Date:  1989 Jul-Aug       Impact factor: 3.352

10.  Automatic segmentation of newborn brain MRI.

Authors:  Neil I Weisenfeld; Simon K Warfield
Journal:  Neuroimage       Date:  2009-05-03       Impact factor: 6.556

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