A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images

The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet transform (NSCT) domain for fusing whole-body PET and CT images. Firstly, the gradient average of each pixel is chosen as the linking strength of PCNN model to implement self-adaptability. Secondly, to improve the fusion performance, the novel sum-modified Laplacian (NSML) and energy of edge (EOE) are extracted as the external inputs of the PCNN models for low- and high-pass subbands, respectively. Lastly, the rule of max region energy is adopted as the fusion rule and different energy templates are employed in the low- and high-pass subbands. The experimental results on whole-body PET and CT data (239 slices contained by each modality) show that the proposed framework outperforms the other six methods in terms of the seven commonly used fusion performance metrics.

[1]  Guanying Wang,et al.  Multimodal medical image fusion using PCNN optimized by the QPSO algorithm , 2016, Appl. Soft Comput..

[2]  Vikrant Bhateja,et al.  NSCT based multispectral medical image fusion model , 2016, 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[3]  Zhenhong Jia,et al.  A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain , 2015 .

[4]  Huiyan Jiang,et al.  A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images , 2017, BioMed research international.

[5]  Chao Wu,et al.  Fusion of remote sensing images based on nonsubsampled contourlet transform and region segmentation , 2011 .

[6]  Hui Liu.  Image Fusion Method based on Non-Subsampled Contourlet Transform , 2012, J. Softw..

[7]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[8]  Vinod Kumar,et al.  Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network , 2015, Biomed. Signal Process. Control..

[9]  屈小波 Xiaobo Qu,et al.  Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain , 2008 .

[10]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[11]  Guo Li Chen,et al.  Fusion algorithm of infrared and visible images based on NSCT and PCNN , 2014, 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI).

[12]  Yi Chai,et al.  Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain , 2011 .

[13]  Yide Ma,et al.  Review of pulse-coupled neural networks , 2010, Image Vis. Comput..

[14]  Qiang Zhang,et al.  Multifocus image fusion using the nonsubsampled contourlet transform , 2009, Signal Process..

[15]  Jingwen Yan,et al.  Image Fusion Algorithm Based on Spatia Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain: Image Fusion Algorithm Based on Spatia Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain , 2009 .

[16]  Vinod Kumar,et al.  CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain , 2014, Journal of Digital Imaging.

[17]  Hamid Behnam,et al.  MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules , 2014, Journal of medical engineering & technology.

[18]  Shutao Li,et al.  Performance comparison of different multi-resolution transforms for image fusion , 2011, Inf. Fusion.

[19]  Zheng Liu,et al.  A new contrast based multimodal medical image fusion framework , 2015, Neurocomputing.

[20]  Li Yan,et al.  A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain , 2015 .

[21]  Malay Kumar Kundu,et al.  NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency , 2012, Medical & Biological Engineering & Computing.

[22]  Hadi Seyedarabi,et al.  A non-reference image fusion metric based on mutual information of image features , 2011, Comput. Electr. Eng..

[23]  Zhizhong Fu,et al.  Infrared and visible images fusion based on RPCA and NSCT , 2016 .

[24]  Cai Xi,et al.  Image fusion algorithm based on adaptive pulse coupled neural networks in curvelet domain , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.