Towards Non-invasive Image-Based Early Diagnosis of Autism

The ultimate goal of this paper is to develop a computer-aided diagnostic (CAD) system for the accurate and early diagnosis of autism spectrum disorders (ASDs) using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 38 infants with a high risk of developing ASDs. The statistical analysis and the diagnostic results (87% accuracy and AUC of 0.96 using random forest classifier) confirm the high performance and the efficiency of the proposed CAD system.