Image Segmentation Techniques Based on Edge Detection and Region Growing

In image processing technology, image segmentation represents a critically important task. For grayscale or RGB images, segmentation techniques based on edge detection and region growing are among the most commonly employed methods. Edge detection involves identifying significant boundary lines within an image, typically implemented using operators like Sobel, Canny, or Prewitt that calculate gradient magnitudes to highlight intensity discontinuities. Region growing, conversely, merges pixels with similar color or texture characteristics into coherent regions through seed point selection and similarity thresholding algorithms. These techniques enable better understanding of objects and structures within images, establishing fundamental groundwork for subsequent image analysis and processing tasks. The combination of these approaches often yields robust segmentation results, with edge information helping to define region boundaries while region growing ensures internal homogeneity.