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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data.

Daniela Lanz, Andre Kaup

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 22, 2019
    PubMed
    Summary

    This study introduces a new method for lossless compression of medical imaging data. The novel approach improves visual quality and reduces data size for dynamic 2-D+t and 3-D+t scans.

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    Area of Science:

    • Medical Imaging
    • Data Compression
    • Computer Vision

    Background:

    • Lossless compression of large, noisy, high bit-depth medical data (2-D+t, 3-D+t) is challenging.
    • Scalable representations are crucial for telemedicine.
    • Motion Compensated Temporal Filtering (MCTF) offers scalability but requires efficient motion encoding.

    Purpose of the Study:

    • To develop an efficient coding scheme for motion information in graph-based motion compensation.
    • To enable performance comparison of graph-based MCTF with traditional methods.
    • To improve lossless compression of dynamic medical data.

    Main Methods:

    • Proposed a novel coding scheme using motion maps to encode adjacency matrices.
    • Integrated graph-based motion compensation into MCTF for medical volume data.

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  • Compared the proposed method against block- and mesh-based approaches.

    • Main Results:

    • The novel motion map coding scheme makes graph-based MCTF feasible for data compression.
    • Achieved higher visual quality (PSNR increase of 0.53-1.90 dB) compared to block- and mesh-based methods for CT and MR data.
    • Simultaneously reduced bit rates for high-quality lowpass subbands.

    Conclusions:

    • Graph-based motion compensation, with the proposed motion map coding, outperforms traditional methods for lossless compression of dynamic medical data.
    • This advancement enhances the efficiency and quality of medical data representation for applications like telemedicine.
    • The method provides a viable solution for compressing complex medical imaging datasets.