A METHOD FOR DETECTING INTERSTRUCTURAL ATROPHY CORRELATION IN MRI BRAIN IMAGES

Distinguishing neurodegenerative diseased patients (e. g., suffering from Alzheimer's Disease (AD)) from healthy individuals with the aid of MRI images is one of the challenges that need to be addressed in the field of Computational Anatomy (CA). A crucial feature in the analysis is the rate of atrophy of brain structures like the hippocampus or the ventricles. Until recently, analysis of atrophy rate has been restricted mainly to 'localized atrophy', i.e. atrophy within one brain structure. Distinguishing correlations of local atrophy rates between different brain structures could possibly provide additional information about the disease process. Therefore, in this paper, we introduce four correlation parameters to measure and analyze correlations of atrophy rate between hippocampus and ventricles. We combine these parameters with three local atrophy rate parameters into a seven-dimensional vector, and use various vector classification methods to see if the methods can distinguish AD patients from normal (NL) subjects in 31 longitudinal MRI baseline images and their follow-ups from the ADNI database. We obtain a good agreement between our classification results and the ground truth data. The analysis is facilitated with the aid of a specially designed graphical user interface.