<title>Fractal coding of medical images</title>

Abstract

In this paper, we investigate the application of fractal concept to the coding of medical images, taking into account the self-similarity at different scales. The approach proposed by Jacquin is very flexible, enabling us to optimize different steps of the associated algorithm. We remark that the choice of the distance used to measure the self-similarity between the range block R_i and the estimated range block R_i is essential to the algorithm. In fact, the choice of the metric determines the optimal parameters of the affine transformation. In our study, we propose two metrics, L₂ and L_(infinity ) and compare their performances. We also develop a simple fast decoding scheme, necessary for a clinical use. This paper addresses the adaptation of the fractal compression algorithm to medical image modalities. We present the results obtained with two image data bases (numerized mammograms and x-ray angiograms). A comparison with JPEG results shows the improvement with our technique, particularly for low bit rates.