Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network

Singh, Shalendra; Gupta, Deep

doi:10.1002/ima.22507

Cited by 6 publications

(3 citation statements)

References 53 publications

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“…Since the C-V model is evolved on the basis of the Mumford-Shah model 24 , the Mumford-Shah model is briefly introduced before the introduction of the C-V model. Researchers proposed a superpixel method to maintain color homogeneity based on global and local boundary advancement of watershed transformation 25 . In the first stage, the flooding priority is calculated by spreading from seed to other pixels.…”

Section: Related Workmentioning

confidence: 99%

Simulation analysis of visual perception model based on pulse coupled neural network

Li¹

2023

Sci Rep

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Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

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Section: Related Workmentioning

confidence: 99%

Simulation analysis of visual perception model based on pulse coupled neural network

Li¹

2023

Sci Rep

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“…Using the non-subsampled shear wave transform (NSST), smooth wavelet transform, and impulsive coupled neural network, Singh and Gupta suggested a multilevel multimodal fusion model [9]. A weighted Laplace pyramid was used to extract structural features from the source image and apply them to an adaptive model that can map the feature weights used for low-band component fusion using absolute maxima and absolute differences, a rule that allows fusion of highfrequency NSST components to preserve complex directional details.…”

Section: Related Workmentioning

confidence: 99%

“…When the RGF takes another approach to recover the edges, that must be the joint RGF iteration, at which time the source output of the filter is represented by t J . When the filter iterates to t times, the output at that time is represented by 1 t J  , and the relationship between them is as in Equation (9).…”

mentioning

confidence: 99%

Application of Medical Brain CT/MRI Image Fusion Algorithm based on Neural Network

Yang¹

2023

IJACSA

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In recent years, fused images have been developed for fast processing of medical images, which provide a more reliable basis for reducing the burden on physicians because they can contain multiple times the image information. In order to achieve fast and accurate recognition results in medical image recognition, avoid similar blocks and shadow fitting in CT/MR fusion images, and improve the entire medical system, in this study, CT/MRI image fusion of brain images is studied based on algorithms generated by Convolutional Neural Network (CNN). The study utilizes Rolling Guidance Filter (RGF) to divide medical CT/MRI images into two parts, one of which is used for model training and the other for image fusion. In the experiments, the results of all three experiments are compared with the Nonsub Sampled Contourlet Transform -Piecewise Convolutional Neural Network (NSCT -PCNN), and the CNN-RGF MI/ IE/SSIM/AG values of CNN-RGF are superior compared to the conventional algorithm of NSCT-RCNN with an average improvement of 10.0% and above, and the resulting CNN-RGF observed meningitis, hydrocephalus, and cerebral infarction with an average of 24.8% higher compared to NSCT-RCNN. The outcomes show that for brain image fusion and detection, the CNN-RGF approach put forward in the study performs better.

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Enhancing Sustainable Development in Medical Image Fusion Using Proposed (EBCE) with DWT

Ghosh,

Jayanthi

2023

Communications in Computer and Information Science

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Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network

Cited by 6 publications

References 53 publications

Simulation analysis of visual perception model based on pulse coupled neural network

Simulation analysis of visual perception model based on pulse coupled neural network

Application of Medical Brain CT/MRI Image Fusion Algorithm based on Neural Network

Enhancing Sustainable Development in Medical Image Fusion Using Proposed (EBCE) with DWT

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