Automatic estimation of registration parameters: image similarity and regularization

T. R. Langerak; U. A. van der Heide; A. N. T. J. Kotte; J. P. W. Pluim

doi:10.1117/12.844180

12 March 2010 Automatic estimation of registration parameters: image similarity and regularization

T. R. Langerak, U. A. van der Heide, A. N. T. J. Kotte, J. P. W. Pluim

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76232S (2010) https://doi.org/10.1117/12.844180
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Image registration is a procedure to spatially align two images that is often used in, for example, computer-aided diagnosis or segmentation applications. To maximize the flexibility of image registration methods, they depend on many registration parameters that must be fine-tuned for each specific application. Tuning parameters is a time-consuming task, that would ideally be performed for each individual registration. However, doing this manually for each registration is too time-consuming, and therefore we would like to do this automatically. This paper proposes a methodology to estimate one of most important parameters in a registration procedure, the regularization setting, on the basis of the image similarity. We test our method on a set of images of prostate cancer patients and show that using the proposed methodology, we can improve the result of image registration when compared to using an average-best parameter.

Citation Download Citation

T. R. Langerak, U. A. van der Heide, A. N. T. J. Kotte, and J. P. W. Pluim "Automatic estimation of registration parameters: image similarity and regularization", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76232S (12 March 2010); https://doi.org/10.1117/12.844180

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