Medical images compression using mixed transforms and vector quantization

The increasing use of modern monitoring devices that generate a vast amount of data requires huge storage capacity. Also, advances in digital medical imaging technologies, particularly magnetic resonance imaging (MRI) and computerized tomography (CT) scans have resulted in a substantial increase in the size of data sets. In order to decrease storage costs of medical images or make them suitable and ready for transmission through common communication channels, the medical image's volume must be reduced. So an effective Medical image compression method is required. Medical imaging poses the greatest challenge of having compression algorithms that reduce the loss of fidelity as much as possible so as not to contribute to diagnostic errors and yet have high compression rates for reduced storage and transmission time. This paper presents an efficient technique for the compression of medical image. In this technique, different mixed transforms based on vector quantization (VQ) algorithm is proposed. The compression algorithms were implemented and tested using multiwavelet, wavelet, and slantlet transforms to form the proposed method based on mixed transforms. Then vector quantization technique was employed to extract the mixed transform coefficients. Simulation results using the MATLAB package showed that the proposed methods gave a high compression ratio (CR) and higher peak signal to noise ratio (PSNR) values for different MRI images and CT scans compared with other available methods. For example, the compression of MRI images gave a result of CR equal to 14.6 with PSNR of 54.4 dB.