MRI Segmentation of Human Brain for Diagnosis of ADHD

Brain-related disease or disorder studies in medical image processing are mainly done by analyzing images obtained from Magnetic Resonance Imaging (MRI). The most crucial stage before identifying any brain illnesses or diseases, such as lesions, Alzheimer's disease, ADHD, epilepsy, and other disorders, is the preprocessing of brain images. The Tl-weighted MRI was used in this study, and the preprocessing steps like contrast enhancement, eliminating noise, and skull extraction are performed in this project. After extensive research, algorithms for denoising and skull stripping were selected. We have implemented the Largest Connected Component (LCC) approach for skull stripping, and also proven that it is very efficient with careful evaluation. After the MRI segmentation process, the segmented grey matter aids in the diagnosis of ADHD. Performance metrics such as Dice coefficients and Jaccard Index are calculated to evaluate our work. In conclusion, this work may be beneficial in identifying between a typically developing brain and one that is affected by ADHD.