Region Of Interest (ROI) coding is a prominent feature of some image coding systems aimed to prioritize specific areas of the image through the construction of a codestream that, decoded at increasing bit-rates, recovers the ROI first and with higher quality than the rest of the image. JPEG2000 is a wavelet-based coding system that is supported in the Digital Imaging and Communications in Medicine (DICOM) standard. Among other features, JPEG2000 provides lossy-to-lossless compression and ROI coding, which are especially relevant to the medical community. But, due to JPEG2000 supported ROI coding methods that guarantee lossless coding are not designed to achieve a high degree of accuracy to prioritize ROIs, they have not been incorporated in the medical community. This paper introduces a ROI coding method that is able to prioritize multiple ROIs at different priorities, guaranteeing lossy-to-lossless coding.The proposed ROI Coding Through Component Prioritization (ROITCOP) method uses techniques of rate-distortion optimization combined with a simple yet effective strategy of ROI allocation that employs the multi-component Preprint submitted to Computer Vision and Image Understanding September 15, 2010Pre-print of: Bartrina, J., Serra, J. and Aulí, F. "JPEG2000 ROI coding through component priority for digital mammography" in Computer vision and image understanding (Elsevier), vol.

Abstract-This paper describes a low-complexity, highefficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors.