Measuring image similarity in the presence of noise

Gustavo Kunde Rohde; Carlos A. Berenstein; Dennis M. Healy Jr.

doi:10.1117/12.594964

29 April 2005 Measuring image similarity in the presence of noise

Gustavo Kunde Rohde, Carlos A. Berenstein, Dennis M. Healy Jr.

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.594964
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Measuring the similarity between discretely sampled intensity values of different images as a function of geometric transformations is necessary for performing automatic image registration. Arbitrary spatial transformations require a continuous model for the intensity values of the discrete images. Because of computation cost most researchers choose to use low order basis functions, such as the linear hat function or low order B-splines, to model the discrete images. Using the theory of random processes we show that low order interpolators cause undesirable local optima artifacts in similarity measures based on the L₂ norm, linear correlation coefficient, and mutual information. We show how these artifacts can be significantly reduced, and at times completely eliminated, by using sinc approximating kernels.

Citation Download Citation

Gustavo Kunde Rohde, Carlos A. Berenstein, and Dennis M. Healy Jr. "Measuring image similarity in the presence of noise", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.594964

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