Scaling techniques for medical image enhancement

In previous work, multiresolution representations that provide a hierarchical framework for analyzing the information content of images were used to obtain contrast enhancement in medical sonograms. Image decomposition and reconstruction was implemented using the two dimensional wavelet transform with a computationally efficient quadrature mirror filter bank architecture. The images were reconstructed at scales likely to exhibit high target energy localization indicating the presence of a tumor. The concepts were applied to B-scan images from in vivo liver images. It was observed that the reconstructed images at scale 1 provided the most enhancement but did not achieve sufficient image contrast. In order to utilize the information present in the adjacent scales, the reconstructed signals are nonlinearly combined using algorithms which exploit the observation that the abnormalities are less echogenic and less sensitive to frequency shifts than the surrounding healthy tissue. It was observed that the frequency diversity techniques outperform the reconstructed images