Medical image segmentation, as an integral component of medical image processing, plays a pivotal role in clinical diagnosis, surgical planning, and treatment program development. Its accuracy directly impacts diagnostic outcomes, enabling precise identification and extraction of anatomical structures or lesion regions from existing medical images. This process assists physicians in quantitative analysis, facilitating enhanced understanding of patient anatomy and pathology for accurate diagnosis and treatment planning. PCNN (Pulse-coupled neural networks), leveraging neuronal activity characteristics, hold significant promise for application in this domain. Hence, this paper proposes an image segmentation method based on VPFC-MSPCNN (Vision-Perceptual FCMSPCNN), derived from the FC-MSPCNN (Fire-controlled MSPCNN) model. This approach simplifies relevant parameters, reduces computational complexity, and effectively controls neuron activity, resulting in higher-quality detection images. Experimental results demonstrate its superior image processing capabilities, establishing it as an effective algorithm for medical image segmentation.
In recent years, there has been a significant amount of research into developing image segmentation methods using neural network models, due to their successful application in the visual field. In this paper, we propose a parameter-continuous fire-controlled MSPCNN (PCFC-MSPCNN) model for image segmentation, which is based on the FCMSPCNN and simplifies related feature parameters. The experimental results indicate that these improvements make the dynamic threshold adjustment of the PCFC-MSPCNN model more appropriate and can provide more accurate results.
Eckhorn obtained a model of a mammalian neuron, the pulse-coupled neural network model (PCNN), by studying neurons in the cat's visual cortex by examining their synchronized pulse oscillations. PCNN is a single-layer neural network model. The dynamic threshold of the model is adjustable, and it has the characteristics of nonlinear modulation coupling, synchronous pulse and dynamic pulse excitation, which makes PCNN model have a good effect on image feature extraction, edge information analysis, image enhancement and image segmentation. In this paper, an image enhancement model called AFC-MSPCNN is proposed for image processing. Aiming at the problems of complex structure of pulse coupled neurons, poor adaptive performance and poor processing results caused by repeated attenuation of image signal threshold in experiments. The improved model reduces the computational complexity and enhances the adaptive capability to some extent. We apply the new model AFC-MSPCNN to image enhancement and experimentally verify its good enhancement effect.
Medical image segmentation has an important role in medical diagnosis, clinical and other social reality scenes, and it is very important significance to reasonably analyze medical images. pulse-coupled neural network (PCNN) has great potential for artificial neural networks with biological background. In this paper, we propose a medical image segmentation method based on a Global fire-controlled MSPCNN (GFC-MSPCNN) in terms of the influence of neurons. The proposed method improve the interaction between the central neurons and their near-neighboring neurons, which is more suitable for human visual characteristics. It is evident from the experiment that the proposed method can obviously reduce the detection cost and improve the detection accuracy, which is an effective medical image segmentation method.
In recent years, driven by the rapid advancements in artificial intelligence technology, image segmentation methods have garnered considerable attention from researchers worldwide. Medical image segmentation, a crucial process, involves accurately extracting structures or regions of interest from medical images. This aids doctors in identifying and quantitatively analyzing various lesions, tissue structures, and organs, thereby playing a pivotal role in medical image analysis and diagnosis. Among these methods, pulse-coupled neural networks (PCNN), leveraging neuronal activity characteristics, have shown significant promise for application. Hence, this paper proposes an optimized FC-MSPCNN (OFC-MSPCNN) image segmentation method, building upon the fire-controlled MSPCNN (FC-MSPCNN), with the aim of simplifying parameter settings. Experimental results demonstrate that this method can notably reduce detection costs while simultaneously enhancing detection accuracy, offering an effective approach for medical image segmentation.
To address the issue of low segmentation accuracy and high computational complexity in traditional pulse-coupled neural network (PCNN) for medical image processing, this paper proposes an image segmentation approach based on unit-linking fire-controlled multi-scale pulse-coupled neural network (ULFC-MSPCNN). This method simplifies the configuration of data parameters and reduces the number of iterations during the effective transmission period. Experimental results demonstrate that the proposed method significantly reduces detection costs while improving detection accuracy. Moreover, it notably diminishes the randomness and unpredictability of discharged neurons. Thus, it can be seen as one of the effective approaches for medical image segmentation.
Mural restoration has an extremely important significance in image restoration. Image enhancement aims to improve image quality, clarity or visualization. The pulse-coupled neural network (PCNN) is a single-layer neural network structure for artificial neural networks, with featuring nonlinear coupling modulation, synchronized pulses, and dynamic pulse excitation. The unique structure and working principle of PCNN enable it to perform well with strong spatial and temporal correlations in image enhancement aspect. The synaptic-linked-FCMSPCNN(SLFC-MSPCNN) is proposed in this paper, and achieves more effective control of neuron firing time by adjusting adaptive parameters. Through related experiments, The proposed SLFC-MSPCNN has good image enhancement performances comparing with popular previous image enhancement approaches in Dunhuang Murals.
In recent years, pulse-coupled neural network (PCNN) has been widely used in the image segmentation field and has obtained relatively satisfactory results. In this paper, based on fire-controlled MSPCNN(FC-MSPCNN), an image segmentation method based on saliency-guided FCMSPCNN(SGFC-MSPCNN) is proposed, which enhances the reasonableness of the variations between the internal activity term and dynamic threshold for the proposed model. and after experimental validation, the method significantly improves the detection accuracy while reducing the detection cost, and thus it is confirmed to be an effective medical image segmentation method.
KEYWORDS: Data modeling, Neurons, Computer simulations, Education and training, Data fusion, Wind speed, Data acquisition, Mathematical optimization, Overfitting, Artificial intelligence
To address the issue of low accuracy in simulating the motion trajectory of water-floating garbage due to multiple factors, a method for simulating the drift trajectory of water-floating garbage based on Sa-LSTM was proposed. The method taking the drift trajectory of water-floating garbage in Lanzhou Section of the Yellow River as the research object, integrated multiple influencing factors through feature derivation and enhanced the memory and generalization ability of LSTM model by using spatial attention module, which further improved the accuracy of water-floating garbage simulation data. The experimental results show that the proposed method can effectively reduce the interference of multiple influencing factors on the simulation of water-floating garbage drifting trajectory, improve the accuracy of drifting trajectory simulation, and provide a method and location information support for the accurate management and management of water-floating garbage.
To address the problems of low segmentation accuracy and machine complexness of ancient pulse coupled neural network (PCNN) in medical image process, a Converged-FCMSPCNN (CFC-MSPCNN) model is projected. Compared with earlier PCNN models, this model additionally optimizes and enhances the synaptic weight matrix, link strength and dynamic threshold, simplifies the parameter settings and reduces the quantity of iterations. In addition, we add a balance parameter Q to regulate the dynamic threshold to improve the model's control over neuronal image processing. Through relevant experiments, we demonstrate that our algorithm has higher results compared with alternative algorithms to accurately section carcinoma lots and considerably reduces the randomness and unpredictability of firing neurons.
In Dunhuang mural image restoration, image enhancement techniques have effectively helped in image restoration. Based on pulse-coupled neural network (PCNN) has been widely used in image processing, in order to solve the low-lighting problem of Dunhuang mural images, on the basis of FC-MSPCNN model, the parameters such as synaptic weight matrix 𝑊ijk1, link strength 𝛽, attenuation factor 𝛼 and attenuation adjustment parameter K are redefined in combination with adaptive parameter setting method, and the Performed-FCMSPCNN (PFC-MSPCNN) model. Finally, the linear transform, gamma transform, and histogram algorithms are used for image enhancement and compared with the PFC-MSPCNN model, respectively. It is verified that the PFC-MSPCNN model in this paper has a good enhancement effect on low-light images.
Most popular image enhancement algorithms are generally based on a series of specialized images collected by image photography devices. Hereinto, Pulse-Coupled Neural Network (PCNN), plays important roles in image enhancement aspect, with lower computational complexity and higher image enhancement accuracy. On the research, we propose an image enhancement method based on enhanced fire-controlled MSPCNN(EFC-MSPCNN) model, which gives the setting methods of designed adaptive parameters. Related experimental results demonstrate that our proposed method has good image enhancement performances.
Medical image segmentation plays an increasingly important role in the whole field of image processing. Among them, the method of tumor segmentation has been paid more attention because of its special clinical significance. To solve the problems of traditional pulse-coupled neural network (PCNN) in the field of medical image processing, an internalactivity-changed FCMSPCNN (IAC-FCMSPCNN) is proposed to segment pulmonary nodules. This method further optimizes and improves the synaptic weight matrix, link strength and dynamic threshold, and reduces the number of model iterations. Experimental verification on five images in PET-CT lung cancer image library shows that the proposed method has good segmentation effect and is more suitable for clinical medical image segmentation.
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