A matrix representation to increase the resolution of a Region of Interest (ROI) for medical images

Diagnostic quality medical images consume vast amounts of network time, system bandwidth and disk storage in current computer architectures. There are many ways in which the use of system and network resources may be optimised without compromising quality of diagnostic image. One of these is in the choice of image representation, both for storage and transfer. In this paper, we show how a particularly flexible method of image representation can lead to an efficient progressive image transmission method. We present the application of a progressive transmission scheme to medical images, and provide some examples of image refinement in a multi-scale fashion. A matrix approach for the entire multiresolution decomposition of images is also presented. Moreover, we prove that thumbnail images created by a multiscale orthogonal decomposition can be optimally interpolated in a minimum square 2-norm sense, based on a generalised Moore-Penrose inverse operator. These interpolators are the minimum energy interpolators which satisfy the undetermined matrix equation in the multiresolution framework. In addition, we present examples of region of interest (ROI) enhancement in which a user selects a square ROI on a low resolution interpolated image.