Image Segmentation Using Spiking Neural Network Based Edge Detection and Seeded Region Growing

Digital image segmentation is a critical step in image analysis, pattern recognition, and computer vision applications. In this study, a hybrid method is proposed to improve the accuracy and efficiency of the segmentation process by combining Spiking Neural Network-based edge detection with the Seeded Region Growing algorithm. Initially, the edges of the image are detected by leveraging the biologically inspired structure of Spiking Neural Networks, allowing for more precise localization of object boundaries. The resulting edge map is then used to guide the Seeded Region Growing algorithm. After edge detection, the remaining homogeneous regions are expanded using Seeded Region Growing, completing the segmentation process. The proposed method has been tested on various images, and the results demonstrate superior performance in terms of edge detection accuracy and region consistency.