An image segmentation method of pulmonary nodules based on IAC-FCMSPCNN

Liqiong Ma; Jinping Li; Wenxiu He; Jing Lian

doi:10.1117/12.2628065

18 February 2022 An image segmentation method of pulmonary nodules based on IAC-FCMSPCNN

Liqiong Ma, Jinping Li, Wenxiu He, Jing Lian

Proceedings Volume 12162, International Conference on High Performance Computing and Communication (HPCCE 2021); 121621C (2022) https://doi.org/10.1117/12.2628065
Event: 2021 International Conference on High Performance Computing and Communication, 2021, Guangzhou, China

Abstract

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.

Citation Download Citation

Liqiong Ma, Jinping Li, Wenxiu He, and Jing Lian "An image segmentation method of pulmonary nodules based on IAC-FCMSPCNN", Proc. SPIE 12162, International Conference on High Performance Computing and Communication (HPCCE 2021), 121621C (18 February 2022); https://doi.org/10.1117/12.2628065

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