Matching curve feature based image registration evaluating method

Abstract

Quantitative evaluation on registration results is an important content in the field of image registration. Many scholars propose to evaluate the registration results with pixel physical coordinates RMSE (root mean square error) and MSE (mean square error), or pixel gray level CC (correlation coefficient) and NMI (normalized mutual information) and the like, however, those methods are normally used for evaluating registration of single-modal or retrospective multi-modal images, but quantitative evaluation results are difficult to give to real multi-modal image registration due to lack of accurate measurement criteria. Through research on image matching curves, the invention provides a novel registration evaluating method, namely a matching curve feature evaluating method. Peaks, peak deviations and peak values of matching curves and RMSEs among the peak values are taken as quantitative evaluation indexes, and quantitative evaluation results are given on the basis of the peak deviations and the peak values. By the method, registration performance is visually described from features of smoothness, sharpness and the like of the curves, registration effect can be evaluated quantitatively via feature indexes of the curves, and given evaluation results for sub-pixel registration are accurate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image registration evaluation method based on matching curve features. Background technique [0002] Image registration is an important link in the field of image analysis. Image registration calculation generally includes five aspects: image preprocessing, registration measurement, optimization algorithm (or matching algorithm), image interpolation, and registration result evaluation. Existing studies have shown that registration metrics can usually be used to quantitatively evaluate the registration effect. For single-modal images, the root mean square error (RMSE), mean square error (MSE), peak signal-to-noise ratio (PSNR), correlation coefficient (CC), and structural similarity (SSIM) of gray scale are generally used; For state images, mutual information (MI or NMI) is generally used to evaluate the registration accuracy. If it is a retrospec...

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Problems solved by technology

[0003] The above evaluation methods have their limitations. Only mutual information, RMSE or MSE of physical coordinates are suitable for retrospective multimodal image registration evaluation; for real multimodal image registration, it is difficult to Give quantitative evaluation results

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