Multimodal medical image registration method fusing gradient information with generalized entropy similarity
A technology of medical image and gradient information, applied in the field of medical image processing, can solve the problems of low registration error and no consideration of image gradient, etc.
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[0072] Such as image 3 As shown, a multimodal medical image registration method that combines gradient information and generalized entropy similarity, the steps are as follows:
[0073] S1, Construct a generalized entropy similarity measure.
[0074] S1.1, define the Arimoto entropy A α ;
[0075]
[0076] Among them, X is a discrete random variable; α is the parameter that controls the non-expandability of Arimoto entropy; M is the number of elements of the discrete random variable X; i is the element number of the probability distribution P of the discrete random variable X; p i is the i-th element of the probability distribution P.
[0077] According to L'Hopital's rule, when α→1, the limit of Arimoto entropy is equal to Shannon entropy, so Shannon entropy can be regarded as a special case of Arimoto entropy.
[0078] S1.2, construct the Jensen-Arimoto divergence (JAD).
[0079]
[0080] In the formula, A α (·) represents the Arimoto entropy, ω i represents th...
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