Pattern visualization and recognition using tensor factorization for early differential diagnosis of parkinsonism

Abstract

Idiopathic Parkinsons disease (PD) and atypical parkinsonian syndromes may have similar symptoms at the early disease stage. Pattern recognition on metabolic imaging has been confirmed of distinct value in the early differential diagnosis of Parkinsonism. However, the principal component analysis (PCA) based method ends up with a unique probability score of each disease pattern. This restricts the exploration of heterogeneous characteristic features for differentiation. There is no visualization of the underlying mechanism to assist the radiologist/neurologist either. We propose a tensor factorization based method to extract the characteristic patterns of the diseases. By decomposing the 3D data, we can capture the intrinsic characteristic pattern in the data. In particular, the disease-related patterns can be visualized individually for the inspection by physicians. The test on PET images of 206 early parkinsonian patients has confirmed differential patterns on the visualized feature images using the proposed method. Computer-aided diagnosis based on multi-class support vector machine (SVM) shown improved diagnostic accuracy of Parkinsonism using the tensor-factorized feature images compared to the state-of-the-art PCA-based scores [Tang et al. Lancet Neurol. 2010].