Comp-denoiser adapted to coronary X-ray images

Compression of coronary angiographic images has been shown to be difficult as compared to other medical imaging modalities. Some of the factors partially responsible for this is the presence of complex detail structures that are only apparent by subtle changes in the contrast and altered by significant amount of noise. Simultaneous compression and denoising is required when images are altered by additive noise. In our work we developed a wavelet based comp-denoiser adapted to coronary X-ray images. The proposed approach consists on integrating an inter-scale dependant thresholding function, using Bayesian estimation theory, with WTCQ coding algorithm. Experimental results show that despite its simplicity and computational efficiency, our method yields a higher compression performance with a superior image quality. It also outperforms the state of the art of compression based denoisers in terms of distortion.