MATLAB Code Implementation for Image Segmentation

This document provides comprehensive details about image segmentation using MATLAB. Below is a source code example demonstrating key image segmentation techniques including edge detection, region-based segmentation, and histogram analysis. These functionalities help users understand the complete image segmentation workflow through practical implementation. Edge detection algorithms (such as Sobel, Canny, or Prewitt operators) identify object boundaries in images by detecting intensity discontinuities. In MATLAB, this can be implemented using functions like edge() with appropriate method selection and parameter tuning to accurately locate transitions between different objects. Region-based segmentation partitions images into homogeneous areas using techniques like watershed transformation or region growing. The implementation typically involves functions such as watershed() or regionprops() to group pixels with similar characteristics (color, texture, intensity) into distinct regions. Histogram analysis examines pixel intensity distribution through functions like imhist(), which generates graphical representations of brightness levels. This helps in threshold selection for segmentation operations and understanding image contrast characteristics through peak and valley analysis in the histogram plot. These additional technical explanations aim to enhance understanding of core concepts and operational procedures in image segmentation, providing both theoretical background and practical MATLAB implementation approaches.