ADAPTIVE REGION GROWING FOR SKULL, BRAIN, AND SCALP SEGMENTATION FROM 3D MRI

摘要

Many of the diseases that are related to the nervous system are serious and can lead to pain, headaches, and many other symptoms. Magnetic resonance imaging (MRI), which produces detailed medical images for the diagnosis of nervous system-related diseases, offers an advanced approach to the study of these diseases. In this paper, a method for the automatic segmentation of the skull, scalp, and brain of 3D MRI imagery is proposed to help doctors diagnose numerous injuries and diseases. For the proposed method, the 3D MRI dataset was first transformed to 2D axial slices, which were grouped based on the skull shape. Second, the histogram method was used to compute the fitting rectangle boundary around the object. Third, the segmentation of the skull was performed using the proposed adaptive region growing method, depending on the slice groups. Finally, the brain and the scalp were segmented. The proposed method was tested and validated on 20 subjects from the BrainWeb database and seven adults from the Neurodevelopmental MRI database with the use of the tissues for the ground truth data. The performance of the proposed method was evaluated using the Dice percentage and a comparison with the other methods.