Batch Images Compression Algorithm Based on the Common Features

Medical images take up huge data space and have slow transmission speed. Medical images have high cost of transmission and take up width channel. Image compression can speed up the transmission of medical images and save up the cost of transmission. They make doctors' diagnosis convenient. The ratio of compression sensing is very slow and the error of the reconstructed images is great. In this study we propose a compressed sensing algorithm based on common features. We use 10 Computed Tomography(CT) images of thoracic cavity and 2 Magnetic Resonance Imaging(MRI) images of heart to proof the result. According to the results, the algorithm is effective in compression and reconstruction. The algorithm has 6.4% improvement in peak signal-to-noise ratio, 100% improvement in compression ratio and the average error has reduced. Accordingly, the algorithm based on common characteristic have more advantages in compression and reconstruction.