Abstract
AbstractImage registration plays an important role in medical image fusion and surgical navigation. Iterative nearest point algorithm (INPA) is a high-precision image registration algorithm, but it also has the problems of huge computation cost and low efficiency. Therefore, we use the differential evolution algorithm to optimize the search process of the iterative nearest point algorithm, so that to improve its registration efficiency. Then a mutation operator selection algorithm based on fitness roughness and a slack selection strategy for differential evolution algorithm were proposed to meet the requirements of high precision and low delay of medical image registration. The registration experiments of chest CT images show that the proposed differential evolution algorithm can not only accelerate the speed of iterative nearest point algorithm, but also improve its registration accuracy. KeywordsDifferential evolution algorithmMedical image registrationIterative nearest point algorithmFitness roughnessSlack selection
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