a novel fusion framework based on adaptive pcnn in nsct domain for whole-body pet and ct images

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ID: 260167
2017
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Abstract
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.
Reference Key
song2017computationala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhiying Song;Huiyan Jiang;Siqi Li
Journal advanced functional materials
Year 2017
DOI
10.1155/2017/8407019
URL
http://dx.doi.org/10.1155/2017/8407019
Keywords
medical informatics computer applications to medicine medical informaticsneurosciences neuropsychiatry biological psychiatry

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