Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer's disease

Background and Objective: F-18-FluoroDeoxyGlucose Positron Emission Tomography (F-18-FDG PET) is one of the imaging biomarkers to diagnose Alzheimer's Disease (AD). In F-18-FDG PET images, the changes of voxels' intensities reflect the differences of glucose rates, therefore voxel intensity is usually used as a feature to distinguish AD from Normal Control (NC), or at earlier stage to distinguish between progressive and stable Mild Cognitive Impairment (pMCI and sMCI). In this paper, F-18-FDG PET images are characterized in an alternative way-the spatial gradient, which is motivated by the observation that the changes of F-18-FDG rates also cause gradient changes. Methods: We improve Histogram of Oriented Gradient (HOG) descriptor to quantify spatial gradients, thereby achieving the goal of diagnosing AD. First, the spatial gradient of F-18-FDG PET image is computed, and then each subject is segmented into different regions by using an anatomical atlas. Second, two types of improved HOG features are extracted from each region, namely Small Scale HOG and Large Scale HOG, then some relevant regions are selected based on a classifier fed with spatial gradient features. Last, an ensemble classification framework is designed to make a decision, which considers the performance of both individual and concatenated selected regions. Results: the evaluation is done on ADNI dataset. The proposed method outperforms other state-of-the-art F-18-FDG PET-based algorithms for AD vs. NC with an accuracy, a sensitivity and a specificity values of 93.65%, 91.22% and 96.25%, respectively. For the case of pMCI vs. sMCI, the three metrics are 75.38%, 74.84% and 77.11%, which is significantly better than most existing methods. Besides, promising results are also achieved for multiple classifications under F-18-FDG PET modality. Conclusions: F-18-FDG PET images can be characterized by spatial gradient features for diagnosing AD and its early stage, and the proposed ensemble framework can enhance the classification performance. (C) 2019 Elsevier B.V. All rights reserved.