Angiographic Image Processing Using Compressive Sampling


Israel N. Rada†, Luis E. Mendoza†, Gonzalo Moreno‡


†Telecommunications Engineering, University of Pamplona
‡ Mechanical Engineering University of Pamplona

Corresponding Author Email: [email protected]

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article presents a robust compression method known as compression sensitivity (CS). CS, allows to reconstruct scattered signals with very few samples unlike the Shannon-Nyquist theorem. In this article the discrete cosine transform and the wavelet transform were used to find most adequate sparse space. Angiographic images were used, which were reconstructed using algorithms such as Large-scale Sparse Reconstruction (SPGL) and Gradient Projection for Sparse Reconstruction (GPRS). In this work, it was demonstrated that using the wavelet-cosine transformed transpose allowed achieving a more satisfactory sparse space than those obtained by other research. Finally, it was demonstrated that CS works in a relevant way for compressing angiographic images and the maximum percentage of error in the reconstruction was 3.56% for SPGL.