Texture-Based Image Segmentation with MATLAB Implementation

This article presents MATLAB source code for texture-based image segmentation and provides detailed explanations of the implementation process using wavelet transforms and K-means clustering. We begin by exploring the fundamental concept of image segmentation and its critical importance in digital image processing applications. Subsequently, we examine the underlying principles and application scenarios of wavelet analysis and K-means clustering algorithms, explaining how these techniques integrate effectively for texture-based segmentation tasks. The MATLAB implementation section covers key algorithmic steps including: wavelet decomposition for texture feature extraction using functions like wavedec2, feature vector construction from wavelet coefficients, clustering parameter optimization for K-means through the kmeans function, and segmentation mask generation. We discuss critical parameter selections such as wavelet type, decomposition level, cluster numbers, and distance metrics. The code structure demonstrates practical implementation of multi-resolution analysis combined with unsupervised learning. Finally, we present real-world application cases and discuss code modification strategies for specific requirements, including performance optimization techniques like feature dimensionality reduction and cluster validity evaluation for improved segmentation accuracy.