In this paper, a combined Fractal and Wavelet (CFW) compression algorithm targeting x-ray angiogram
images is proposed. Initially, the image is decomposed using wavelet transform. The smoothness of the low frequency
part of the image appears as an approximation image with higher self similarities, therefore, it is coded using a fractal
coding technique. However, the rest of the image is coded using an adaptive wavelet thresholding technique. This model
is implemented and its performance is compared with best performances of the available published algorithms. A data set
containing 1000 x-ray angiograms is used to study the performance of the algorithm. A minimum compression ratio of 30
with a peak signal to noise ratio (PSNR) of 36 dB and percent diameter stenosis deviation of (<0.2%) was achieved.
Results demonstrate the effectiveness of the proposed technique in obtaining a diagnostic quality of reconstructed images
at very low bit rates.