Functional Brain Image Clustering and Edge Analysis of Acute Stroke Speech Arrest MRI

Abstract

In the area of neural imaging the statistical and Mathematical analysis plays an important role in supervising the fMRI images of human brain. In this work, we have performed a cluster analysis of fMRI images of human brain with a connectivity architecture that takes the sequence of brain images. In particular, the edge extent is a major challenging piece of work in brain edge detection planning and it’s quantitative estimation. Edge detection is a fundamentally focus on to distinguish the tissues :WM, GM and CSF the signal intensities are abruptly changes along edge. Here, we have discussed the effectiveness of the procedure to location intensity of brain image and edge detection tasks. Cluster analysis for fMRI images with brain connectivity architecture that takes the sequence of brain image data. (i) Simulation of how clustering can be used for neuroimaging atlas to parcellate the brain. (ii) Investigated the quantitative evaluation of ROI. (iii) Analyzed the intensity in arbitrary frame from video data of speech arrest MRI. Due to overlapped voxel at the edges of brain region are not defined by specific intensities therefore we focus on intensities of MR images of the and performed the clustering using Python. Finally, we have compared the intensities of the MR images histogram.In the distribution of all the voxel intensities the realted intensities of and WM, GM,and CSF voxel respectively in the histogram are used the gradient based method to distinguish the distribution respectively with spatial prior = 0.1. We demonstrate the gradient information contains contrast and intensity of the brain image.