Grid Computing Application for Brain Magnetic Resonance Image Processing

Magnetic Resonance Image Processing

With the growth of 3D MRI, and the popularity of 4D functional MRI, there is a considerable need for advanced post-processing software to handle the extremely large volumes of data and to extract meaningful analyses. The primary analysis of such information is visualisation-the images are viewed by experts who can often judge immediately the significance of certain complex relationships. With v...

A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

This paper presents a novel idea of intracranial segmentation of magnetic resonance (MR) brain image using pixel intensity values by optimum boundary point detection (OBPD) method. The newly proposed (OBPD) method consists of three steps. Firstly, the brain only portion is extracted from the whole MR brain image. The brain only portion mainly contains three regions – grey matter (GM), white mat...

Magnetic Resonance Imaging (Mri) Simulation On A Grid Computing Architecture

In this paper, we present the implementation of a Magnetic Resonance Imaging (MRI) simulator on a GRID computing architecture. The simulation process is based on the resolution of Bloch equation [1] in a 3D space. The computation kernel of the simulator is distributed to the grid nodes using MPICH-G2 [2]. The results presented show that simulation of 3D MRI data is achieved with a reasonable co...

Magnetic Resonance Image Post-Processing for Multiple Sclerosis Research

Image Processing for Diffusion Tensor Magnetic Resonance Imaging

This paper describes image processing techniques for Diffusion Tensor Magnetic Resonance. In Diffusion Tensor MRI, a tensor describing local water diffusion is acquired for each voxel. The geometric nature of the diffusion tensors can quantitatively characterize the local structure in tissues such as bone, muscles, and white matter of the brain. The close relationship between local image struct...