A lung cancer tumor image segmentation method of a CFC-MSPCNN based on PET/CT

Jing Lian; Xuefeng Yang; Xiaojiao Niu; Yuzhu Cao

doi:10.1117/12.2684384

1 August 2023 A lung cancer tumor image segmentation method of a CFC-MSPCNN based on PET/CT

Jing Lian, Xuefeng Yang, Xiaojiao Niu, Yuzhu Cao

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127540E (2023) https://doi.org/10.1117/12.2684384
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

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.

Citation Download Citation

Jing Lian, Xuefeng Yang, Xiaojiao Niu, and Yuzhu Cao "A lung cancer tumor image segmentation method of a CFC-MSPCNN based on PET/CT", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127540E (1 August 2023); https://doi.org/10.1117/12.2684384

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KEYWORDS

Image segmentation

Image processing

Neurons

Image processing algorithms and systems

Lung cancer

Medical imaging

Tumor growth modeling

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