Respiratory Exertion Using Virtual Reality

Abstract

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumors. The large amount of data produced by MRI prevents manual segmentation in a reasonable time. Automatic and reliable segmentation methods are required. Independent projection-based classification (LIPC) is used to segment the tumor region. Here, also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Here, proposed an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3x3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against over fitting, given the fewer number of weights in the network. Results reported on public benchmarks reveal that our architecture achieves competitive accuracy compared to the state- of-the-art brain tumor segmentation methods while being computationally efficient.. Index Terms—Digital Image Processing,Positron Emission Tomography,PhotomultiplierTube,ComputedTomography,Magne tic Resonance Image,Fluid-Attenuated Inversion Recovery ,Single Photon Emission Computed Tomography TR Repetition Time, echo time ,Region of Interest.