Mutual Information-based Multimodal Image Registration Using Gaussian Function
Wufan Chen · Zhongguo yixue wulixue zazhi · 2010
Objective:Maximization of mutual information is a popular similarity measure for medical image registration.In the calculation of mutual information image,people often use partial volume interpolation to update the joint histogram for each pixel pair to avoid the introduction of the new gray value.However,this method may cause some local extreme values when the image is translated by integer point,leading to many errors in image registration.Methods:This paper proposed to use the new algorithm which calculated the joint histogram with the Gaussian function.The smoothness of the Gaussian function can avoid statistical errors of the image joint histogram.The best optimization parameters were find using the Powell optimization method.Results:Experimental results which are complete using CT-PET experimental data indicate the proposed algorithm effectively eliminate the local extremum and improve the accuracy of medical image registration.Moreover,this algorithm is also applicable to noise image registration.Conclusions:The method meets multimodal image registration,overcomes the lack of traditional method,improves the accuracy of results.