An automatic image co-registration algorithm based on signal correlation function and artificial neural network

Jun Liu, Shan'an Zhu, He Bin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Objective: To co-register multi-modal serial images by non-rigid transforming. Method: A new conception of signal process was introduced to the procedure of medical image registration. The edge of two frames of medical images as two rows of random signals that have time delay characteristics was described. With the correlation function of the signal as the measure, the transform relationship between the two images was optimized by means of an artificial neural network. This method was successfully developed for brain image co-registration. Result: Computer simulations were conducted and the simulation results demonstrated that the co-registration error was smaller than one pixel. Furthermore, the present method had fewer parameters to be optimized, less time consumed and were more automatical than other co-registration methods. Finally, it was demonstrated that the present method can successfully co-register the post-operative CT images with the pre-operative MRI images in a patient's undergoing neurosurgical operation. Conclusion: This method provides a new useful tool for multi-modal medical images co-registration.

Original languageEnglish (US)
Pages (from-to)425-429
Number of pages5
JournalHangtian Yixue Yu Yixue Gongcheng/Space Medicine and Medical Engineering
Volume19
Issue number6
StatePublished - Dec 2006

Keywords

  • Correlation function
  • Data fusion
  • Image registration
  • Neural network

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