A novel functional MRI-based immersive tool for dementia disease severity prediction via spectral clustering and incremental learning

An image-based immersive tool is proposed to diagnose the severity of the dementia disease based on arterial spin labeling images for the first time in this paper. A Gaussian form of mahalanobis distance is incorporated to measure the pair-wise similarity of images, and its with-in full matrix is automatically determined via spectral clustering and incremental learning techniques. There are totally 350 demented patients belonging to three groups, i.e., Alzheimer´s disease, mild cognitive impairment, and non cognitive impairment, utilized for experimental evaluation. Several other methods are also implemented and compared with the newly proposed method from the statistical perspective. It is concluded that the new method outperforms others in this dementia diagnosis study.