Hierarchical finite state vector quantization for MRI and CT image compression

Magnetic resonance imaging (MRI) and computer tomography (CT) are very important techniques that are used in disease diagnosis in medicine. In an average sized hospital, many tera-bytes of digital imaging data (MRI and CT) are generated every year, almost all of which has to be kept and archived. The compression of medical images is currently performed by using different algorithms. The most common compression technique is vector quantization. Interframe coding, the discrete Hartley transform, mixed transform, entropy-coded DPCM and JPEG algorithm are also used. Hierarchical finite-state vector quantization (HFSVQ), which is the improved version of vector quantization, has been introduced as a compression technique with high compression ratios for video images. Although the HFSVQ algorithm is the most efficient compression technique according to its compression ratios, we show that it has also a good performance for brain tomography and magnetic resonance images