| Summary: | Three dimensional (3D) and four dimensional (4D) medical images are increasingly being used in many clinical and research applications. Due to their huge file size, 3D and 4D medical images pose heavy demands on storage and archiving resources. Lossless compression methods usually facilitate the access and reduce the storage burden of such data, while avoiding any loss of valuable clinical data. In this thesis, we propose novel methods for highly efficient storage and scalable access of 3D and 4D medical imaging data that outperform the state-of the-art. Specifically, we propose (1) a symmetry-based technique for scalable lossless compression of 3D medical images; (2) a 3D scalable medical image compression method with optimized volume of interest (VOI) coding; (3) a motion-compensation-based technique for lossless compression of 4D medical images; and (4) a lossless functional magnetic resonance imaging (fMRI) compression method based on motion compensation and customized entropy coding.
The proposed symmetry-based technique for scalable lossless compression of 3D medical images employs wavelet transform technology and a prediction method to reduce the energy of the wavelet sub-bands based on a set of axes of symmetry. We achieve VOI coding by employing an optimization technique that maximizes reconstruction quality of a VOI at any bit-rate, while incorporating partial background information and allowing for gradual increase in peripheral quality around the VOI.
The proposed lossless compression method for 4D medical imaging data employs motion compensation and estimation to exploit the spatial and temporal correlations of 4D medical images. Similarly, the proposed fMRI lossless compression method employs a motion compensation process that uses a 4D search, bi-directional prediction and variable-size block matching for motion estimation; and a new context-based adaptive binary arithmetic coder to compress the residual and motion vector data generated by the motion compensation process.
We demonstrate that the proposed methods achieve a superior compression performance compared to the state-of-the-art, including JPEG2000 and 3D-JPEG2000.
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