A novel Diffusion-Weighted Image analysis system for pediatric metabolic brain diseases

The diffusion weighted imaging (DWI) technique can be utilized to investigate a variety of diseases. We propose an automated system which assists the diagnosis of metabolic brain diseases clinically. In this study, DWI images are preprocessed and exponential apparent diffusion coefficient (eADC) images are produced. The eADC images are later brain extracted and normalized to a standard brain atlas. Subsequently, we utilized wavelets to denoise the eADC images. The images are rectified, thresholded and conspicuous abnormal regions are identified. Abnormal regions constitute the features that will be used by a fuzzy relational classifier in order to categorize the diseases. The sensitivity, positive predictivity and specificity of 60%, 60% and 93.33%, respectively in detecting metabolic brain diseases have been achieved.