Many methods have been proposed to classify the MR brain images automatically. We have proposed a method based on a Neural Network ...

In brain image processing applications the skull stripping is an essential part to explore. In numerous medical image applications the skull stripping stage act as a pre-processing step as due to this stage the accuracy of diagnosis increases in the manifold. The MR image skull stripping stage removes the non-brain tissues from the brain part such as dura, skull, and scalp. Nowadays MRI is an emerging method for brain imaging. However, the existence of the skull region in the MR brain image and the low contrast are the two main drawbacks of magnetic resonance imaging. Therefore, we have proposed a method for contrast enhancement of brain MRI using histogram equalization techniques. While morphological image processing technique is used for skull stripping from MR brain image. We have implemented our proposed methodology in the MATLAB R2015a platform. Peak signal to noise ratio, Signal to noise ratio, Mean absolute error, Root mean square error has been used to evaluate the results of our presented method. The experimental results illustrate that our proposed method effectively enhance the image and remove the skull from the brain MRI.

Many algorithms have been proposed for the solution of the minimum vertex cover (MVC) problem, but the researchers are unable to find the optimality of an approximation algorithm. In this paper, we have proposed a method to evaluate that either the result returned by an approximation algorithm for the minimum vertex cover problem is optimal or not. The proposed method is tested on three algorithms, i.e., maximum degree greedy (MDG) algorithm, modified vertex support algorithm (MVSA) and clever steady strategy algorithm (CSSA). The proposed method provides an opportunity to test the optimality of an approximation algorithm for MVC problem with low computation complexity. The proposed method has performed well during experimentation, and its results brighten the path of successful implementation of the method for the evaluation of approximation algorithms for the minimum vertex cover (MVC) problem. The testing of the proposed method was carried out on small graph instances. The proposed method has resolved the problem to test the optimality of the approximation algorithm for the minimum vertex cover problem. This technique has digitized the process of finding out the accuracy of the optimal solution returned by approximation algorithms for MVC.