Texture Image Segmentation Using Wavelet Transform Implementation

I have developed a custom algorithm for texture image segmentation using wavelet transforms. The implementation involves three key computational stages: first, performing a three-level wavelet decomposition on the input image using functions like wavedec2() or similar wavelet transformation methods. Each decomposition level generates approximation and detail coefficients that capture texture characteristics at different scales. The core innovation lies in the hierarchical clustering approach: at each decomposition level, I apply clustering algorithms (such as k-means or hierarchical clustering) to group similar wavelet coefficients. The clustering results from each level then serve as priors to guide the classification process at subsequent, finer-resolution levels. This multiscale approach allows for robust texture discrimination. To further enhance segmentation quality, I integrate complementary image processing techniques including edge detection algorithms (like Canny or Sobel operators) for boundary refinement and color mapping functions for better visualization of segmented regions. The algorithm employs matrix operations for efficient coefficient manipulation and uses morphological operations for post-processing refinement. This wavelet-based segmentation framework demonstrates strong performance in texture discrimination and can be adapted for various applications including medical image analysis (tissue segmentation) and object recognition systems. The method effectively handles texture variations through its multiscale analysis approach, producing accurate and clearly defined segmentation results across different image types.