A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

Li, Yang; Zhao, Junqiao

doi:10.1109/access.2020.3019426

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The pulse-coupled neural networks (PCNN) are biology-inspired models derived from the mammalian visual cortex (Chen and Shibata, 2010 ). Owing to the simple calculation of the PCNN, it is widely applied in image segmentation (Kuntimad and Ranganath, 1999 ), image shadow removal (Gu et al, 2005 ), object detection (Ranganath and Kuntimad, 1999 ), and image fusion (Li and Zhao, 2020 ).…”

Section: The Pulse-coupled Mizh Spiking Model With Cortical-like Coll...mentioning

confidence: 99%

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

2022

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The Izhikevich (IZH) spiking neuron model can display spiking and bursting behaviors of neurons. Based on the switching property and bio-plausibility of the memristor, the memristive Izhikevich (MIZH) spiking neuron model is built. Firstly, the MIZH spiking model is introduced and used to generate 23 spiking patterns. We compare the 23 spiking patterns produced by the IZH and MIZH spiking models. Secondly, the MIZH spiking model actively reproduces various neuronal behaviors, including the excitatory cortical neurons, the inhibitory cortical neurons, and other cortical neurons. Finally, the collective dynamic activities of the MIZH neuronal network are performed, and the MIZH oscillatory network is constructed. Experimental results illustrate that the constructed MIZH spiking neuron model performs high firing frequency and good frequency adaptation. The model can easily simulate various spiking and bursting patterns of distinct neurons in the brain. The MIZH neuronal network realizes the synchronous and asynchronous collective behaviors. The MIZH oscillatory network can memorize and retrieve the information patterns correctly and efficiently with high retrieval accuracy.

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Section: The Pulse-coupled Mizh Spiking Model With Cortical-like Coll...mentioning

confidence: 99%

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

2022

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“…PCNNs are represented as single-layer networks involving multiple neuron connections, which inevitably leads to computational complexity, low time efficiency and poor fusion accuracy. To improve the efficiency of image fusion, a multi-channel-PCNN (m-PCNN) [41] can solve the above problems. Its main feature is that it can complete the high-frequency fusion of multiple images with only one drive, which can be mathematically described by the following equation,…”

Section: High Frequency Fusionmentioning

confidence: 99%

An image fusion-based method for recovering the 3D shape of roll surface defects

Xu,

Lou

et al. 2023

Meas. Sci. Technol.

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Most of the existing studies on roll surface defects focus on qualitative detection and lack of quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are stylus method, laser scanning method and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform (NSST) is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called Mm-PCNN to obtain a fully focused image. The method achieves 3D shape recovery of defects by modelling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. The experimental results show that the proposed method effectively integrates detailed information and achieves a reconstruction error control of less than 4%, thus ensuring higher accuracy and improved noise immunity.

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“…Contourlet Transform (CT) method in reference [13], through the directional anisotropy and local information property of contour wave, solves the problem of poor performance of sub-band directional information and contour structure in image fusion; the reference [14] proposes Non-Subsampled Contourlet Transform (NSCT) based on CT solves the translation invariance while inheriting the advantages of CT; the reference [15] completes the fusion of multimodal medical images by NSCT; the reference [16] fuses two difference remote sensing images by NSCT for natural disaster change detection. Meanwhile, artificial neural networks [17] and convolutional neural networks [18] are also widely used in image fusion to improve image fusion quality.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Reconstruction Method Based on Parameter Adaptive Dual-Channel Pulse-Coupled Neural Network to Optimize Multiscale Decomposition

Tang

Zhang

et al. 2023

IEEE Access

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Existing image processing methods usually divide image denoising and image fusion into two directions for research, and even the best current image denoising methods such as DnCNN can cause information loss during image processing, and the image fusion method mainly considers the fusion between multiple source images to complement the image information, but does not take into account the degradation of the fusion quality due to the noise in the source images. Therefore, aiming at the problem that various existing image denoising methods cannot reduce noise efficiently in complex noise situations while performing multi-source image fusion, preserving texture details, highlighting edge contour structures, and enriching image energy, a method of reconstructing remote sensing images by simplified adaptive dual-channel PCNN (Dual-PCNN) fusion in the NSCT transform domain is proposed to unify image noise reduction and image fusion under the same framework to obtain a noisy image information reconstruction method, which completes the complementary advantages between two image processing methods. Firstly, the impulse noise in the image is removed using IMFLED filtering, and then the Gaussian noise is processed by DnCNN and FFDNet respectively, and the results are imported into simplified parametric adaptive dual-channel PCNN to fuse the preprocessed images respectively. Then the DnCNN image and the preprocessed image are decomposed by NSCT to obtain each low-frequency sub-band and high-frequency sub-band. The low-frequency sub-band is stimulated with detail using the guided filter, and the high-frequency sub-band is enhanced by separating the detail layer through the guided filter to obtain the energy-detail-enhanced high-frequency sub-band. Finally, the corresponding sub-bands are fused into the simplified parametric adaptive dual-channel PCNN respectively, and the fused sub-band coefficients are reconstructed by NSCT inversion to obtain the final reconstructed remote sensing image. The experiments on grayscale images and remote sensing images show that this method achieves excellent results in both visual subjective and quantitative index evaluation, and the reconstructed images perform well in texture details, contour structure, and energy enrichment. After the information reconstruction of remote sensing images, the quality and resolution of remote sensing images are effectively improved, so that terrain information, landform features, and structural features can be extracted more accurately, which are widely used in remote sensing and geographic information fields such as landform analysis, agricultural monitoring, building inspection and environmental protection.INDEX TERMS Image reconstruction, non-subsampled contourlet transform (NSCT), pulse-coupled neural network (PCNN), convolutional neural network (CNN), guided filter.

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A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

Cited by 6 publications

References 24 publications

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

An image fusion-based method for recovering the 3D shape of roll surface defects

Remote Sensing Image Reconstruction Method Based on Parameter Adaptive Dual-Channel Pulse-Coupled Neural Network to Optimize Multiscale Decomposition

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