Volumetric medical image compression with three-dimensional wavelet transform and octave zerotree coding

Compression of 3D or 4D medical image data has now become imperative for clinical picture archiving and communication systems (PACS), telemedicine and telepresence networks. While lossless compression is often desired, lossy compression techniques are gaining acceptance for medical applications, provided that clinically important information can be preserved in the coding process. We present a comprehensive study of volumetric image compression with three-dimensional wavelet transform, adaptive quantization with 3D spatial constraints, and octave zerotree coding. The volumetric image data is first decomposed using 3D separable wavelet filterbanks. In this study, we adopt a 3-level decomposition to form a 22-band multiresolution pyramid of octree. An adaptive quantization with 3D spatial constraints is then applied to reduce the statistical and psychovisual redundancies in the subbands. Finally, to exploit the dependencies among the quantized subband coefficients resulting from 3D wavelet decomposition, an octave zerotree coding scheme is developed. The proposed volumetric image compression scheme is applied to a set of real CT medical data. Significant coding gain has been achieved that demonstrates the effectiveness of the proposed volumetric image compression scheme for medical as well as other applications.