Computer Science ›› 2020, Vol. 47 ›› Issue (10): 194-199.doi: 10.11896/jsjkx.190700185

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Brain CT and MRI Image Fusion Based on Morphological Image Enhancement and PCNN

LI Chang-xing1, LEI Liu2, ZHANG Xiao-lu2   

  1. 1 School of Science,Xi’an University of Posts and Telecommunications,Xi’an 710121,China
    2 School of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China
  • Received:2019-07-25 Revised:2019-10-27 Online:2020-10-15 Published:2020-10-16
  • About author:LI Chang-xing,born in 1962,postgra-duate,professor,postgraduate supervisor.His main research interests include the research of matrix theory and application of wavelet transform in image compression.
    LEI Liu,born in 1993,postgraduate.Her main research interests include di-gital image information processing and so on.
  • Supported by:
    Natural Science Basic Research Plan in Shaanxi Province of China (2018JM1054)

Abstract: To compensate for the problems that arise during brain CT and MRI image fusion,such as the false Gibbs pheno-menon,the lack of detailed information,ringing,a fusion method of brain CT and MRI image based on morphological image enhancement and the PCNN(Pulse Coupled Neural Network) is proposed.Firstly,the source image is enhanced by open and closed operation based on morphology.The enhancement processed image is input into the PCNN fusion model as an input stimulus of the PCNN receiving domain,to determine the final weight map of the model output.Finnaly,a clear and easily processed image is formed.Experimental results show that the proposed method is superior to other methods in maintaining image edge clearness,preserving effective information and balancing redundancy.Compared with the unenhanced PCNN method,the average gradient and spatial frequency of image after morphological enhancement and PCNN fusion increases by 24.59% and 42.56% respectively.Compared with the image fusion based on Laplacian method,the standard deviation increases by 16.67%.

Key words: Morphological image enhancement, Pulse coupled neural network model, Fusion weight, Link strength, Medical image fusion

CLC Number: 

  • TP391
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