Detection of hyperintense regions on MR brain images using a Mamdani type Fuzzy Rule-Based System: Application to the detection of small multiple sclerosis lesions

2011 ◽  
Author(s):  
F. X. Aymerich ◽  
P. Sobrevilla ◽  
E. Montseny ◽  
A. Rovira
Keyword(s):  
Multiple Sclerosis ◽  
System Application ◽  
Fuzzy Rule ◽  
Brain Images ◽  
Rule Based ◽  
Rule Based System ◽  
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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