Compression of three-dimensional (3D) or four-dimensional 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 coherently three-dimensional method for volumetric image compression using 3D wavelet transform and 3D zerotree coding. First, the volumetric image data is decomposed using 3D separable wavelet filterbanks. In this study, we adopt a three-level decomposition to form a 22-band multiresolution pyramid of an octree. Then, to exploit the dependencies among the subband coefficients resulting from 3D wavelet decomposition, a 3D zerotree coding scheme is utilized. To take advantage of the near-Laplacian distributions of the subband coefficients and the efficiency of zerotree coding, a pseudouniform quantization is adopted. The proposed volumetric image compression scheme is applied to two sets of real CT medical data. Significant coding gains have been achieved which demonstrate the effectiveness of the proposed volumetric image compression scheme for medical as well as other applications.