MRI Brain Tumor Image Segmentation Using Morphological Methods and Region Growing Techniques

This implementation performs MRI brain tumor image segmentation using morphological methods and region growing techniques. These approaches effectively extract different brain tumor regions, assisting medical professionals in more accurate diagnosis and treatment planning. The morphological method applies mathematical morphology principles through erosion and dilation operations to separate brain tumors from surrounding tissues. In code implementation, this typically involves using structuring elements and sequential operations like imopen() and imclose() functions to enhance tumor boundaries. The region growing method performs image segmentation based on pixel similarity characteristics. Algorithm implementation requires setting appropriate threshold values and growth criteria to merge adjacent pixels into homogeneous regions. This can be programmed using seed point selection and neighborhood connectivity checks, often implemented through queue-based pixel aggregation algorithms. The combination of these methods significantly improves segmentation accuracy and stability. The morphological preprocessing enhances image quality by removing noise and filling gaps, while region growing provides precise tumor boundary detection. This integrated approach supports advanced research and applications in medical imaging, with potential implementations using MATLAB's image processing toolbox or Python's scikit-image library for clinical validation studies.