Brain Region Segmentation with CNN and Advanced Filtering usingGlobally Guided Image Filtering and NLM Filter

Abstract

In this paper, Brain Region segmentation is implemented in which from the MRI images brain region needs to be detected. This gives importance in MRI correction and diagnosis. Many researchers have worked on machine learning and deep learning technique using the image processing tools in MATLAB. The effort is done to achieve higher accuracy and lowest possible error. Brain Region Segmentation is amongst best research areas which gives tremendous scope of study and analysis for the scientists. Firstly, the brain region is segmented using convolutional neural network is implemented. As the new work novelty, the proposed technique is defined using globally guide image filtering and WLS filter is combined with convolutional neural network for the better accuracy in brain region segmentation. This technique improved more parameters such as sensitivity, specificity and mean square error. The combination of globally guided image filtering and WLS filter gives the interesting noise mitigation and smoothening function to the brain region image from MRI. The deep learning method is improvised by the use of filters GGIF, WLS and NLM combination and offers the better segmentation images as verified in results using MATLAB.