Brain MRI Image Segmentation Using Region Growing Algorithm with Single Seed Point

In this article, we explore a specific application of image segmentation algorithms: the region growing method. The primary objective of this algorithm is to identify regions of interest around a single seed point within brain MRI images. The implementation typically involves initializing from the seed pixel and iteratively adding neighboring pixels based on similarity criteria, such as intensity thresholds or texture properties.

During this process, we employ various techniques and algorithms to process and optimize the images. Key implementation aspects include defining appropriate similarity measures, managing the growth queue using data structures like FIFO queues or priority queues, and handling boundary conditions. We also discuss how to fine-tune algorithm parameters—such as intensity tolerance thresholds, connectivity rules (4-connected or 8-connected neighborhoods), and stopping criteria—to achieve optimal segmentation results.

Ultimately, we demonstrate how this method effectively segments brain MRI images to facilitate better understanding and analysis of anatomical structures. The algorithm's output can be further enhanced through post-processing steps like morphological operations to smooth boundaries and remove small disconnected regions.