Open AccessMedical image fusion using the PCNN based on IQPSO in NSST domain
Author(s) -
Gai Di,
Shen Xuanjing,
Chen Haipeng,
Xie Zeyu,
Su Pengxiang
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0040
Subject(s) - image fusion , fusion , image (mathematics) , artificial intelligence , set (abstract data type) , frequency domain , computer science , pattern recognition (psychology) , artificial neural network , particle swarm optimization , energy (signal processing) , fusion rules , domain (mathematical analysis) , computer vision , algorithm , mathematics , statistics , mathematical analysis , philosophy , linguistics , programming language
In this study, an improved quantum‐behaved particle swarm optimisation based pulse‐coupled neural network (IQPSO‐PCNN) is proposed in the non‐subsampled shearlet transform (NSST) domain for medical image fusion. First, NSST tool is used to decompose the source image into low‐frequency and high‐frequency subbands. Then, for low‐frequency subbands, the fusion rules of two different functions are presented, which simultaneously addresses two key issues of energy preservation and detail extraction. For high‐frequency subbands, unlike conventional PCNN‐based methods, parameters are manually set based on experience, and the decomposed high‐frequency subbands share a set of parameters. The IQPSO‐PCNN model can obtain the optimal parameters for each high‐frequency subband adaptively according to its own information. Finally, the fused low‐frequency subband and high‐frequency subbands are inversely transformed by NSST to acquire the final fused image. The proposed algorithm uses >90 pairs of images with four different modalities. In addition, fusion experiments are performed on different sequences of the three modes. The experimental results demonstrate that the proposed method is superior to existing state‐of‐art methods in subjective visual performance and objective evaluation.