Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

OBJECTIVE This paper presents Visual MRI, an innovative tool for the magnetic resonance imaging (MRI) analysis of tumoral tissues. The main goal of the analysis is to separate each magnetic resonance image in meaningful clusters, highlighting zones which are more probably related with the cancer evolution. Such non-invasive analysis serves to address novel cancer treatments, resulting in a less...

Towards Information Visualization and Clustering Techniques for MRI Data Sets

The paper deals with the integrated use of Information Visualization techniques and clustering algorithms to analyze Magnetic Resonance Imaging (MRI) data sets. The paper also describes the criteria we followed in designing and implementing the prototype, according to the above approach. Finally, some preliminary results are given for the considered medical application.

MRI ventilation analysis by merging parametric active contours

Parametric and non-parametric statistical analysis of DT-MRI data.

In this work parametric and non-parametric statistical methods are proposed to analyze Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. A Multivariate Normal Distribution is proposed as a parametric statistical model of diffusion tensor data when magnitude MR images contain no artifacts other than Johnson noise. We test this model using Monte Carlo (MC) simulations of DT-MRI experimen...

Non-Parametric Image Subtraction for MRI

Image subtraction is used in many areas of machine vision to identify small changes between equivalent pairs of images. Often only a small subset of the differences will be of interest. For example, MS lesions can be detected by subtraction of MRI scans taken before and after the injection of GdDTPA contrast agent. The contrast agent highlights the lesions, but also results in global changes in...