Medical image registration method based on alpha-Renyi mutual information

Abstract

The present invention discloses a medical image registration method based on the alpha-Renyi mutual information (RMIR). The method comprises the following steps: the step of acquiring image pixel information: acquiring pixel information of a reference image and a floating image; the step of acquiring image grayscale information: acquiring grayscale information of the reference image and the floating image; the step of acquiring image mutual information: acquiring the Renyi mutual information of the reference image and the floating image; and the step of image registration: performing rigid registration on the Renyi mutual information obtained in the step of acquiring image mutual information, and obtaining the optimal transformation through the registration function, so that the Renyi mutual information reaches a maximum value. The Renyi entropy is used to introduce the mutual information metric, and the registration function is used to register, so that the registration accuracy is effectively improved; and the appropriate alpha initial parameters are found out through a large number of registration experiments, so that the operation load in the registration process is effectivelyreduced.

Description

technical field [0001] The invention relates to an image processing method, in particular to a medical image registration method. Background technique [0002] In the process of diagnosing a patient, it is often necessary to analyze multiple images of the same patient together to obtain comprehensive information about the patient in order to improve the accuracy of medical diagnosis. The spatial geometric transformation is carried out on the state image, so that the pixels (voxels) representing the same result are consistent in space. Usually, there are registration methods based on image features and image grayscale. The registration method based on image grayscale uses image Gray information, registration accuracy is high, such as Shannon mutual information for registration (Medical Image Registration based on the Shannon mutual information, SMIR), but because the mutual information function contains a large number of local minima, it is easy to fall into a local optimum, ...

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Problems solved by technology

[0002] In the process of diagnosing a patient, it is often necessary to analyze multiple images of the same patient together to obtain comprehensive information about the patient in order to improve the accuracy of medical diagnosis. The spatial geometric transformation is carried out on the state image, so that the pixels (voxels) representing the same result are consistent in space. Usually, there are registration methods based on image features and image grayscale. The registration method based on image grayscale uses image Gray information, registration accuracy is high, such as Shannon mutual information for registration (Medical Image Registration based on the Shannon mutual information, SMIR), but because the mutual information function contains a large number of local minima, it is easy to fall into a local optimum, and will Continuously iteratively searching the solution space, the amount of calculation is huge, and if the initial value is not selected properly, it will consume more search time and even cause registration failure

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