Zernike Moment and Mutual Information Based Methods for Multimodal Image Registration

Kashyap, Suraj Kumar; Jat, Dinesh; Bhuyan, M. K.; Vishwakarma, Amit; Gadde, Prathik

doi:10.1007/978-981-32-9291-8_9

Cited by 1 publication

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“…It is a statistical measure that evaluates joint intensity distribution. Although it was proposed 25 years ago, it is still considered optimal and selected for the vast majority of multi-modality image registration algorithms [8][9][10]. The weakness of mutual information as a similarity measure is its high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Comparison of mutual information and its point similarity implementation for image registration

Chehade

Rogelj

2021

IJECE

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Mutual information (MI) is one of the most popular and widely used similarity measures in image registration. In traditional registration processes, MI is computed in each optimization step to measure the similarity between the reference image and the moving image. The presumption is that whenever MI reaches its highest value, this corresponds to the best match. This paper shows that this presumption is not always valid and this leads to registration error. To overcome this problem, we propose to use point similarity measures (PSM) which in contrast to MI allows constant intensity dependence estimates called point similarity functions (PSF). We compare MI and PSM similarity measures in terms of registration misalignment errors. The result of the comparison confirms that the best alignment is not at the highest value of MI but near to it and it shows that PSM performs better than MI if PSF matches the correct intensity dependence between images. This opens a new direction of research towards the improvement of image registration.

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Section: Introductionmentioning

confidence: 99%