Joao M. SantosGuarda, AndréM. M. Rodrigues, NunoFaria, Sergio2025-06-302025-06-302015-12J. M. Santos, A. F. R. Guarda, N. M. M. Rodrigues and S. M. M. Faria, "Contributions to lossless coding of medical images using minimum rate predictors," 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 2015, pp. 2935-2939, doi: 10.1109/ICIP.2015.7351340.http://hdl.handle.net/10400.8/13474Article number - 7351340Medical imaging compression is experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like MRI or CT. Furthermore, legal and diagnosis restrictions impose the use of lossless compression and data archival for several years. These facts create a demand for more efficient compression tools, used for archiving and communication. In this work, we first evaluate the performance of traditional medical image compression algorithms against that of recent state of the art lossless image encoders. We then propose a method to improve the Minimum Rate Predictors lossless encoder, by exploiting inter picture redundancy in volumetric anatomical images. Results show that the proposed method is more efficient than state of the art encoders, such as HEVC, by about 28.8%, and achieves a gain of up to 57.8% in compression ratio when compared with traditional methods.engMedical imaging compression is experiencing a growth in terms of usage and image resolutionnamely in diagnostics systems that require a large set of imageslike MRI or CT. Furthermorelegal and diagnosis restrictions impose the use of lossless compression and data archival for several years. These facts create a demand for more efficient compression toolsused for archiving and communication. In this workwe first evaluate the performance of traditional medical image compression algorithms against that of recent state of the art lossless image encoders. We then propose a method to improve the Minimum Rate Predictors lossless encoderby exploiting inter picture redundancy in volumetric anatomical images. Results show that the proposed method is more efficient than state of the art encoderssuch as HEVCby about 28.8%and achieves a gain of up to 57.8% in compression ratio when compared with traditional methods.conference paper10.1109/icip.2015.7351340