Wavelet-Based Texture Segmentation

Wavelet-based texture segmentation is a widely used image processing technique that extracts texture features from different regions of an image to facilitate area identification and analysis. This method finds applications in various domains including medical imaging analysis, industrial inspection, and computer vision. The implementation typically involves applying wavelet transform to decompose the image into multi-scale frequency bands, effectively capturing texture details at different resolutions. In code implementation, this can be achieved using functions like wavedec2() in MATLAB or PyWavelets in Python for 2D discrete wavelet decomposition. Subsequent processing involves applying thresholding techniques (such as Otsu's method or adaptive thresholding) to the wavelet coefficients in different sub-bands, followed by segmentation algorithms like watershed or region growing to generate the final texture segmentation results. The method's effectiveness stems from wavelet's ability to provide both frequency and spatial information, making it a robust and widely adopted solution for texture analysis in digital image processing.