Genetic algorithm for feature selection of MR brain images using wavelet co-occurence

2011 ◽  
Author(s):  
Ahmed Kharrat ◽  
Nacéra Benamrane ◽  
Mohamed B. Messaoud ◽  
Mohamed Abid
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Selection Of

Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding

2015 ◽  
Vol 6 (3) ◽  
pp. 33-48 ◽  
Author(s):  
Ouarda Assas
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Brain Images ◽  
Image Thresholding ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Optimum Threshold ◽  
Selection Of

Thresholding is a fundamental task and a challenge for many image analysis and pre-processing process. However, the automatic selection of an optimum threshold has remained a challenge in image segmentation. The fuzzy 2-partition entropy approach for threshold selection is one of the best image thresholding techniques. In this work, an improvement of the later method using type-2 fuzzy sets is proposed to represent the imprecision or lack of knowledge of the expert in the choice of the membership function associated with the image. Two databases are used to evaluate its effectiveness: dataset of standard grayscale test images and MR Brain images. Experiment results show that the type-2 Fuzzy 2-partition entropy algorithm performs equally well in terms of the quality of image segmentation and leads to a good visual result.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

2017 ◽  
Vol 24 (6) ◽  
pp. 653-659
Author(s):  
Qiang Zheng ◽  
Honglun Li ◽  
Baode Fan ◽  
Shuanhu Wu ◽  
Jindong Xu
Keyword(s):  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Brain Images ◽  
Model Based ◽  
Contour Model ◽  
Mr Brain Images ◽  
Mr Brain

Automatic segmentation of MR brain images of preterm infants using supervised classification

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 628-641 ◽  
Author(s):  
Pim Moeskops ◽  
Manon J.N.L. Benders ◽  
Sabina M. Chiţǎ ◽  
Karina J. Kersbergen ◽  
Floris Groenendaal ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Supervised Classification ◽  
Automatic Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain
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