Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain

Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.

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

[2]  Belur V. Dasarathy,et al.  Medical Image Fusion: A survey of the state of the art , 2013, Inf. Fusion.

[3]  Ferrante Neri,et al.  An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems , 2014, Int. J. Neural Syst..

[4]  Xiaohui Luo,et al.  Dendrite P systems , 2020, Neural Networks.

[5]  Peng Shi,et al.  Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems , 2018, IEEE Transactions on Smart Grid.

[6]  G. Easley,et al.  Sparse directional image representations using the discrete shearlet transform , 2008 .

[7]  R. S. Anand,et al.  Ripplet domain fusion approach for CT and MR medical image information , 2018, Biomedical Signal Processing and Control.

[8]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[9]  Xun Chen,et al.  Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain , 2019, IEEE Transactions on Instrumentation and Measurement.

[10]  Luciano Alparone,et al.  Remote sensing image fusion using the curvelet transform , 2007, Inf. Fusion.

[11]  Yu Liu,et al.  Simultaneous image fusion and denoising with adaptive sparse representation , 2015, IET Image Process..

[12]  Yiteng Pan,et al.  A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation , 2019, Multimedia Tools and Applications.

[13]  H. Adeli,et al.  Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology , 2010 .

[14]  Hong Peng,et al.  Fault diagnosis of power systems using fuzzy tissue-like P systems , 2017, Integr. Comput. Aided Eng..

[15]  Hong Peng,et al.  Spiking neural P systems with multiple channels , 2017, Neural Networks.

[16]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[17]  Yu Liu,et al.  IFCNN: A general image fusion framework based on convolutional neural network , 2020, Inf. Fusion.

[18]  Hong Peng,et al.  Dynamic threshold neural P systems , 2019, Knowl. Based Syst..

[19]  Gabriel Cristóbal,et al.  Multifocus image fusion using the log-Gabor transform and a Multisize Windows technique , 2009, Inf. Fusion.

[20]  Xu Zhang,et al.  A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks , 2016, Int. J. Neural Syst..

[21]  Yuki Todo,et al.  Neurons with Multiplicative Interactions of Nonlinear Synapses , 2019, Int. J. Neural Syst..

[22]  Yu Liu,et al.  A general framework for image fusion based on multi-scale transform and sparse representation , 2015, Inf. Fusion.

[23]  Meenu Manchanda,et al.  An improved multimodal medical image fusion algorithm based on fuzzy transform , 2018, J. Vis. Commun. Image Represent..

[24]  Jingyu Hou,et al.  Multimodal sensor medical image fusion based on nonsubsampled shearlet transform and S-PCNNs in HSV space , 2018, Signal Process..

[25]  Hui Li,et al.  DenseFuse: A Fusion Approach to Infrared and Visible Images , 2018, IEEE Transactions on Image Processing.

[26]  Meenu Manchanda,et al.  A novel method of multimodal medical image fusion using fuzzy transform , 2016, J. Vis. Commun. Image Represent..

[27]  Yi Chai,et al.  A novel dictionary learning approach for multi-modality medical image fusion , 2016, Neurocomputing.

[28]  Kai Zeng,et al.  Perceptual Quality Assessment for Multi-Exposure Image Fusion , 2015, IEEE Transactions on Image Processing.

[29]  Gheorghe Paun,et al.  Spiking Neural P Systems with Anti-Spikes , 2009, Int. J. Comput. Commun. Control.

[30]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .

[31]  Cedric Nishan Canagarajah,et al.  Pixel- and region-based image fusion with complex wavelets , 2007, Inf. Fusion.

[32]  Baohua Zhang,et al.  Multi-focus image fusion algorithm based on compound PCNN in Surfacelet domain , 2014 .

[33]  Dapeng Tao,et al.  Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning , 2018, Pattern Recognit..

[34]  Remco J. Renken,et al.  Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson's Disease in 3D Nuclear Imaging Data , 2019, Int. J. Neural Syst..

[35]  Vladimir S. Petrovic,et al.  Gradient-based multiresolution image fusion , 2004, IEEE Transactions on Image Processing.

[36]  Ashish Khare,et al.  Fusion of multimodal medical images using Daubechies complex wavelet transform - A multiresolution approach , 2014, Inf. Fusion.

[37]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

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

[39]  Rabab K. Ward,et al.  Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis , 2019, IEEE Signal Processing Letters.

[40]  Zhengyou He,et al.  Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems , 2015, IEEE Transactions on Power Systems.

[41]  Hong Peng,et al.  Spiking neural P systems with inhibitory rules , 2020, Knowl. Based Syst..

[42]  Alexander Toet,et al.  Image fusion by a ration of low-pass pyramid , 1989, Pattern Recognit. Lett..

[43]  Linqiang Pan,et al.  Spiking Neural P Systems With Rules on Synapses Working in Maximum Spiking Strategy , 2014, IEEE Transactions on NanoBioscience.

[44]  Hong Peng,et al.  Coupled Neural P Systems , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[45]  Yi Liu,et al.  Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review , 2018, Inf. Fusion.