Texture analysis of brain MRI and classification with BPN for the diagnosis of dementia

Abstract

In this paper, we present an evaluation of diagnosis of dementia using texture analysis of brain MRI with wavelets and further classification by BackPropagation Network. The tests were conducted on 3D brain MRI data extracted from OASIS database. The classification is based on the following steps: First, the region of interest is extracted from the MRI images by wavelets, Gray level occurance matrix (GLCM) and Haralick features. Gabor features were characterized by the distribution of histogram of wavelet coefficients. These features were segregated into three datasets. The first dataset containing the GLCM features, the second data set has the Haralick features and the third dataset has Gabor wavelet based Haralick features. Classification was done by backpropagation network based on 3 feature vectors. From the analysis it has been found that the average efficiency of Gabor combined with Haralick features is around 97% for all types of datasets, and the average efficiency value for GLCM is 86 % and that of Haralick features was 90%. From the comparison of the average efficiency of the wavelet families, statistical features extracted from Gabor wavlets provides better efficiency than the other two methods. © 2011 Springer-Verlag Berlin Heidelberg.