Pulse-coupled neural networks and parameter optimization methods

Xiaoli, Xu; Wang, Guanying; Ding, Shifei; Cheng, Yuhu; Wang, Xuesong

doi:10.1007/s00521-016-2397-2

Cited by 20 publications

(9 citation statements)

References 56 publications

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“…However, all these fusion approaches suffer from the selection of free parameters of PCNN because of its dependency on the nature and texture of the input images. Conventionally, the values of the PCNN parameters are selected based on successive trials, but recently Xu et al [28] presented different methods related to the selection of the PCNN parameters. Moreover, the other PCNN-based approaches are presented using a adapting linking parameter based on local contrast, entropy (EN), directional gradient, saliency, fractional dimension, local visibility and intensity of pixels or coefficients [16,[28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

et al. 2020

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Diagnostic medical imaging plays an imperative role in clinical assessment and treatment of medical abnormalities. The fusion of multimodal medical images merges complementary information present in the multi-source images and provides a better interpretation with improved diagnostic accuracy. This paper presents a CT-MR neurological image fusion method using an optimised biologically inspired neural network in nonsubsampled shearlet (NSST) domain. NSST decomposed coefficients are utilised to activate the optimised neural model using particle swarm optimisation method and to generate the firing maps. Low and high-frequency NSST subbands get fused using max-rule based on firing maps. In the optimisation process, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image. To analyse the fusion performance, extensive experiments are conducted on the different CT-MR neurological image dataset. Objective performance is evaluated based on different metrics to highlight the clarity, contrast, correlation, visual quality, complementary information, salient information, and edge information present in the fused images. Experimental results show that the proposed method is able to provide better-fused images and outperforms other existing methods in both visual and quantitative assessments.

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Section: Related Workmentioning

confidence: 99%

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

et al. 2020

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“…

{normalα}_{normalT}

and

V_{normalT}

denote the attenuation coefficient and threshold magnitude coefficient, respectively. In the traditional simplified PCNN model, adaptive linking strength

normalβ

and adaptive threshold amplitude coefficient

V_{normalT}

are main aspects of the self‐adaptive adjustment on PCNN . The link strength coefficient

normalβ

adjusts the extent to which neighboring neurons affect central neurons.…”

Section: Related Workmentioning

confidence: 99%

“…In the traditional simplified PCNN model, adaptive linking strength b and adaptive threshold amplitude coefficient V T are main aspects of the self-adaptive adjustment on PCNN. 23 The link strength coefficient b adjusts the extent to which neighboring neurons affect central neurons. The large b causes widespread pulse synchronization.…”

Section: Pulse Coupled Neural Networkmentioning

confidence: 99%

IGM‐based perceptual multimodal medical image fusion using free energy motivated adaptive PCNN

Tang

Tian

2017

Int J Imaging Syst Tech

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Multimodal medical image fusion merges two medical images to produce a visual enhanced fused image, to provide more accurate comprehensive pathological information to doctors for better diagnosis and treatment. In this article, we present a perceptual multimodal medical image fusion method with free energy (FE) motivated adaptive pulse coupled neural network (PCNN) by employing Internal Generative Mechanism (IGM). First, source images are divided into predicted layers and detail layers with Bayesian prediction model. Then to retain human visual system inspired features, FE is used to motivate the PCNN for processing detail layers, and large firing times are selected as coefficients. The predicted layers are fused with the averaging strategy as activity level measurement. Finally, the fused image is reconstructed by merging coefficients in both fused layers. Experimental results visually and quantitatively show that the proposed fusion strategy is superior to the state‐of‐the‐art methods.

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“…The pulse-coupled neural network (PCNN) has been studied very extensively. As an effective nonlinear digital data analysis method, it is well capable of isolating noisy pixel points and eliminating high-intensity noise during image processing [8]. In this paper, we are committed to the study of noise reduction effect of PCNN and make improvements to the PCNN method.…”

Section: Introductionmentioning

confidence: 99%

Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

Min

Wang

et al. 2018

Applied Sciences

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Aimed at the problem of poor noise reduction effect and parameter uncertainty of pulse-coupled neural network (PCNN), a hybrid image denoising method, using an adaptive PCNN that has been optimized by grey wolf optimization (GWO) and bidimensional empirical mode decomposition (BEMD), is presented. The BEMD is used to decompose the original image into multilayer image components. After a GWO is run to complete PCNN parameter optimization, an adaptive PCNN filter method is used to remediate the polluted noise points that correspond to the different image components, from which a reconstruction of the denoised image components can then be obtained. From an analysis of the image denoising results, the main advantages of the proposed method are as follows: (i) the method effectively solves the deficiencies that arise from the critical PCNN parameter determination issue; (ii) the method effectively overcomes the problem of high-intensity noise effects by providing a more targeted and efficient noise reduction process; (iii) when using this method, the noise points are isolated, and the original pixel points are restored well, which can lead to preservation of image detail information. When compared with traditional image denoising process algorithms, the proposed method can yield a better noise suppression effect, based on indicators including analysis of mutual information (MI), structural similarity (SSIM), the peak signal-to-noise ratio (PSNR) and the standard deviation (STD). The feasibility and applicability of the proposed denoising algorithm are also demonstrated experimentally.

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Pulse-coupled neural networks and parameter optimization methods

Cited by 20 publications

References 56 publications

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

IGM‐based perceptual multimodal medical image fusion using free energy motivated adaptive PCNN

Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

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