Implementing a large-scale multicentric study for evaluation of lossy JPEG and JPEG2000 medical image compression: challenges and rewards
David Koff, Peter Bak, Andrew Volkening, Harry S. Shulman, Paul Brownrigg, Luigi Lepanto, Tracy Michalak, ALEX J. KISS · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
At modest compression ratios, lossy compression schemes allow substantial image size reduction without a significant loss in visual information. This is a consequence of the coding engines' transformation (such as the Discrete Cosine Transfomation (DCT) and the Discrete Wavelet Transform (DWT) in combination with quantization and truncation operations which all exploit the characteristics of the human visual system to achieve file-size reduction. The objective of our study was to determine levels of lossy compression that can be confidently used in diagnostic imaging. We conducted an extensive clinical evaluation using a standardized methodology incorporating two recognized evaluation techniques: Diagnostic Accuracy with Receiver Operating Characteristic (ROC) Analysis and Original-Revealed Forced Choice. Images covering 5 modalities and 7 anatomical regions were compressed at 3 different levels using JPEG and JPEG 2000 compression algorithms. To enable radiologists across Canada to evaluate images for our study, we developed a dedicated software application that was synchronized to a centralized server; which allowed results were reported, in real-time, to the central database via the Internet. In order to obtain findings that were relevant to everyday clinical evaluation, images were not viewed under a strict laboratory environment, but rather they were read under typical viewing conditions that comply with current standards of practice. We present here the methodology and specific technology developed for the purpose of this study, we explain the specific problems that we have encountered during the implementation and we give preliminary results. Our preliminary findings suggest that the most appropriate compression algorithm and compression ratios are largely dependent on the image specifics including the type/ modality and anatomical region studied.