Medical image segmentation application based on VPFC-MSPCNN

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.