Roi-Based Multiresolution Compression Of Heart Mr Images

In this paper, we present an image compression scheme based on the automatic segmentation of regions of interest (RoI) and a lossy wavelet compression algorithm adapted to this segmentation. Quasi-lossless compression is applied to the RoI while lossy compression is allowed outside the RoI, preserving at best the visual quality of the decoded image within a defined RoI. In fact, for diagnostic accuracy purposes, quasi-lossless compression is often mandatory, while high compression ratio can only be achieved by lossy compression methods. The proposed technique is applied to heart MR images where the RoI is the entire heart. First, an unsupervised segmentation of the heart is performed in the original MR images, and the RoI is modeled by an ellipse fitted by means of a genetic algorithm. This model is defined by only 5 parameters, providing an efficient representation of the surrounding shape of the RoI. Compression is then applied using a wavelet-based multiresolution scheme. The quantization factor applied to the wavelet coefficients is adapted to the region and the subband, leading to the quasi-lossless compression in the RoI and lossy compression outside this RoI. The quantization difference between inside and outside RoI is also optimized for the desired compression ratio. Finally, the compressed bitstream is transmitted to the decoder together with the parameters of the RoI, allowing the reconstruction of the RoI surrounding shape in the decoder. Results of this RoI-based compression scheme are presented and further compared with the JPEG standard.