Scalable Compression of 3D Medical Images with Optimized Volume of Interest Coding
Victor Sanchez, The University of British Columbia, Vancouver, Canada

Seminar • 08 March 2010 • CO017

Abstract
Volumetric medical images, such as magnetic resonance imaging (MRI) and computed tomography (CT) sequences, are becoming a standard in healthcare systems and an integral part of a patient’s medical record. Due to the vast amount of resources needed for archival and communication of these 3D data, it has become essential to employ compression methods that provide lossless reconstruction, random access, and scalability by quality and resolution. Lossless reconstruction is especially important to avoid any loss of valuable clinical data, which may result in medical and legal implications. Random access and scalability, on the other hand, are especially important in telemedicine applications, where clients with limited bandwidth using a remote image retrieval system may connect to a central server to access a specific region of a 3D medical image, i.e., a volume of interest (VOI), at different qualities and resolutions. In this presentation, we introduce a novel 3D medical image compression method with a) scalability properties, by quality and resolution up to lossless reconstruction; and b) optimized VOI coding capabilities at any bit-rate. We are particularly interested in interactive telemedicine applications, where a VOI is usually transmitted from a central server to a client at the highest quality possible, preferably in conjunction with a low quality version of the background, which is important in a contextual sense to help the client observe the position of the VOI within the original 3D image. We will present the coding techniques employed by our proposed method, which include a 3D integer wavelet transform, embedded block coding with optimized truncation and 3D contexts, a bit-stream reordering procedure, and a VOI coding optimization technique. We will also demonstrate that the proposed method achieves a better coding performance, in terms of the peak signal-to-noise ratio, than that achieved by the two state-of-the-art region of interest coding methods adopted by the JPEG2000 standard, MAXSHIFT and general scaling-based methods.