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19 July 2024 Medical image segmentation application based on OFC-MSPCNN
Miao Li, Xiangyu Wei, Nana Ren, Yuzhu Cao, Jing Lian
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Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130T (2024) https://doi.org/10.1117/12.3035146
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Miao Li, Xiangyu Wei, Nana Ren, Yuzhu Cao, and Jing Lian "Medical image segmentation application based on OFC-MSPCNN", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130T (19 July 2024); https://doi.org/10.1117/12.3035146
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KEYWORDS
Image segmentation
Medical imaging
Image processing
Neurons
Image processing algorithms and systems
Biomedical applications
Image enhancement
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