Literature DB >> 18218522

Multispectral analysis of the brain using magnetic resonance imaging.

T Taxt1, A Lundervold.   

Abstract

The authors demonstrate an improved differentiation of the most common tissue types in the human brain and surrounding structures by quantitative validation using multispectral analysis of magnetic resonance images. This is made possible by a combination of a special training technique and an increase in the number of magnetic resonance channel images with different pulse acquisition parameters. The authors give a description of the tissue-specific multivariate statistical distributions of the pixel intensity values and discuss how their properties may be explored to improve the statistical modeling further. A statistical method to estimate the tissue-specific longitudinal and transverse relaxation times is also given. It is concluded that multispectral analysis of magnetic resonance images is a valuable tool to recognize the most common normal tissue types in the brain and surrounding structures.

Entities:  

Year:  1994        PMID: 18218522     DOI: 10.1109/42.310878

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  6 in total

1.  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

2.  Volume and shape in feature space on adaptive FCM in MRI segmentation.

Authors:  Renjie He; Balasrinivasa Rao Sajja; Sushmita Datta; Ponnada A Narayana
Journal:  Ann Biomed Eng       Date:  2008-06-24       Impact factor: 3.934

3.  Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

Authors:  Renjie He; Sushmita Datta; Balasrinivasa Rao Sajja; Ponnada A Narayana
Journal:  Comput Med Imaging Graph       Date:  2008-04-02       Impact factor: 4.790

4.  Multivariate characterization of white matter heterogeneity in autism spectrum disorder.

Authors:  D C Dean; N Lange; B G Travers; M B Prigge; N Matsunami; K A Kellett; A Freeman; K L Kane; N Adluru; D P M Tromp; D J Destiche; D Samsin; B A Zielinski; P T Fletcher; J S Anderson; A L Froehlich; M F Leppert; E D Bigler; J E Lainhart; A L Alexander
Journal:  Neuroimage Clin       Date:  2017-01-06       Impact factor: 4.881

5.  Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification.

Authors:  Favour Ekong; Yongbin Yu; Rutherford Agbeshi Patamia; Xiao Feng; Qian Tang; Pinaki Mazumder; Jingye Cai
Journal:  Diagnostics (Basel)       Date:  2022-07-07

6.  Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients' DTI data - Theory, simulations and example cases.

Authors:  Gyula Gyebnár; Zoltán Klimaj; László Entz; Dániel Fabó; Gábor Rudas; Péter Barsi; Lajos R Kozák
Journal:  PLoS One       Date:  2019-09-23       Impact factor: 3.240

  6 in total

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