Applications of Wavelet Transform in Registration, Segmentation, Denoising, and Compression of Medical Images

Wavelet transforms and other multiscale analysis functions have been used for compact signal and image representations in denoising, compression, and feature detection processing problems. The wavelet transform itself offers great design flexibility. Basis selection, spatial-frequency tiling, and various wavelet threshold strategies can be optimized for best adaptation to a processing application, data characteristics, and feature of interest. Fast implementation of wavelet transforms using a filter-bank framework enables real-time processing capability. Instead of trying to replace standard image processing techniques, wavelet transforms offer an efficient representation of the signal, finely tuned to its intrinsic properties. By combining such representations with simple processing techniques in the transform domain, multiscale analysis can accomplish remarkable performance and efficiency for many image processing problems.