Texture anisotropy analysis of brain scans.

Currently, the world population is aging. People over 75 is one of the fastest growing age groups. This is the group most affected by Alzheimer's disease. Reliable early diagnosis and tracking methods are essential to assist therapy and prevention. This research aims to study anisotropy texture in tomographic brain scans to diagnose and quantify the severity of Alzheimer's disease. A full methodology to study computer tomography, magnetic resonance imaging and multispectral magnetic resonance imaging is presented in this thesis. Before applying any texture method to the tomographic brain images, a segmentation technique has to be used to extract the different regions of interest. We propose the use of connected filters and iterative region merging to perform the segmentation. Gradient vector histogram is applied to study the texture anisotropy of computer tomography scans. Computer tomography scans present evidence of texture changes in demented subjects compare to normal subjects. The overlap between these groups is considerable, so anisotropy texture using computer tomography does not seem to add more useful information to the diagnosis of Alzheimer's disease than other clinical criteria. Another method to study texture anisotropy is grey-level dependance histogram, which is based in a 3D generalisation for arbitrary orientation of the 2D co-occurrence matrices. This texture technique is applied to magnetic resonance imaging scans, where features extracted from the grey matter component have a strong correlation with the mini mental state examination1 scores. Finally, Multispectral Grey-Level Dependence Histogram (MGLDH), Absolute Difference Histogram (ADH) and spatial correlations are texture techniques designed to study multispectral images. These techniques are applied to multispectral magnetic resonance images. We evaluate the performance of the different multispectral texture methods, and compare them with single channel texture methods