Wavelets and fuzzy relational classifiers: 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 pilot system, which assists in the diagnosis of metabolic brain diseases, utilizing the DWI. 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 template. Subsequently, we utilized wavelets to denoise the eADC images. The images are rectified, thresholded and now conspicuous abnormal regions are subsequently identified utilizing different brain atlases. Abnormal regions constitute the features that will be used by a fuzzy relational classifier in order to categorize the diseases. A sensitivity and specificity of 60% and 93.33%, respectively, in detecting metabolic brain diseases have been achieved.