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.
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A medical image segmentation method for GFC-MSPCNN
Semantic Scholar · Medicine · 2024
Abstract
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.