scholarly journals A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis

2013 ◽  
Vol 2 ◽  
pp. 184-196 ◽  
Author(s):  
Sushmita Datta ◽  
Ponnada A. Narayana
Keyword(s):  
Multiple Sclerosis ◽  
Three Dimensional ◽  
Comprehensive Approach ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

Three-dimensional anatomical model-based segmentation of MR brain images through principal axes registration

1995 ◽  
Vol 42 (11) ◽  
pp. 1069-1078 ◽  
Author(s):  
L.K. Arata ◽  
A.P. Dhawan ◽  
J.P. Broderick ◽  
M.F. Gaskil-Shipley ◽  
A.V. Levy ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Brain Images ◽  
Anatomical Model ◽  
Model Based ◽  
Principal Axes ◽  
Mr Brain Images ◽  
Mr Brain

A New Image Segmentation Method Based on Three-Dimensional Neural Network

2012 ◽  
Vol 490-495 ◽  
pp. 157-161
Author(s):  
Guo Fu Lin
Keyword(s):  
Image Segmentation ◽  
Classification Accuracy ◽  
Probabilistic Approach ◽  
Three Dimensional ◽  
Training Method ◽  
Segmentation Method ◽  
Brain Images ◽  
Feature Sets ◽  
Mr Brain Images ◽  
Mr Brain

In this paper, a three-dimensional probabilistic approach for MR brain image segmentation is proposed. Based on the noise-free representative reference vectors provided by SOM, the results of the 3D-PNN method are superior to other traditional algorithms. In addition to the 3D-PNN architecture, a fast three-step training method is proposed. The proposed approach also incorporates structure tensor to find appropriate feature sets for the 3D-PNN with respect to resulting classification accuracy. Computational results with simulated MR brain images have shown the promising performance of the proposed method.

Automated segmentation of three-dimensional MR brain images

2006 ◽  
Author(s):  
Jonggeun Park ◽  
Byungjun Baek ◽  
Choong-Il Ahn ◽  
Kyo Bum Ku ◽  
Dong Kyun Jeong ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Automated Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

An evaluation of the significance of areas of intense signal in the MR brain images of patients with multiple sclerosis

1993 ◽  
Vol 11 (3) ◽  
pp. 311-317 ◽  
Author(s):  
I.J. Namer ◽  
O. Yu ◽  
Y. Mauss ◽  
B.E. Dumitresco ◽  
J. Chambron
Keyword(s):  
Multiple Sclerosis ◽  
Brain Images ◽  
Mr Brain Images ◽  
Intense Signal ◽  
Mr Brain

scholarly journals CLUSTERING MULTIPLE SCLEROSIS SUBGROUPS WITH MULTIFRACTAL METHODS AND SELF-ORGANIZING MAP ALGORITHM

Fractals ◽  
2017 ◽  
Vol 25 (04) ◽  
pp. 1740001 ◽  
Author(s):  
YELIZ KARACA ◽  
CARLO CATTANI
Keyword(s):  
Multiple Sclerosis ◽  
Self Organizing Map ◽  
Mr Images ◽  
Brain Images ◽  
Som Algorithm ◽  
Mr Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Brain ◽  
Multifractal Method ◽  
Self Organizing

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

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