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Why Does Mutual-Information Work for Image Registration? A Deterministic Explanation.

Hemant D Tagare, Murali Rao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 11, 2015
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    Summary
    This summary is machine-generated.

    This study explains mutual information (MI) image registration by demonstrating it aligns image partitions. New, non-entropy-based objective functions derived from this theory also achieve effective image registration.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Image Processing

    Background:

    • Mutual information (MI) based image registration is a widely used technique.
    • The underlying principles of why MI registration is effective have not been fully elucidated.
    • Existing methods primarily rely on entropy-based metrics.

    Purpose of the Study:

    • To propose a deterministic explanation for mutual-information-based image registration.
    • To introduce a novel partition-alignment theory for image registration.
    • To explore alternative, non-entropy-based objective functions for image registration.

    Main Methods:

    • Developed a partition-alignment theory for image registration.
    • Investigated the relationship between partition alignment and concepts like Schur- and quasi-convexity.
    • Proposed and simulated novel objective functions for image registration.

    Main Results:

    • Demonstrated that MI registration functions by aligning specific image partitions.
    • Showcased that the proposed non-entropy-based objective functions perform well in noisy image registration simulations.
    • Validated the partition-alignment theory through experimental results.

    Conclusions:

    • The partition-alignment theory provides a deterministic explanation for MI image registration.
    • The theory facilitates the development of new, effective image registration objective functions.
    • This work opens new avenues for research in image registration methodologies.