Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

Medical image segmentation is a challenging task especially in multimodality medical image analysis. In this paper, an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer (MFGWO-PCNN) is proposed for multimodality medical image segmentation. Specifically, a tw...

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Bibliographic Details
Published in:Journal of bionics engineering Vol. 18; no. 3; pp. 711 - 720
Main Authors: Wang, Xue, Li, Zhanshan, Kang, Heng, Huang, Yongping, Gai, Di
Format: Journal Article
Language:English
Published: Singapore Springer Singapore 01.05.2021
Subjects:
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Engineering
grey wolf optimizer
pulse coupled neural network
medical image segmentation
bionic algorithm
ISSN:1672-6529, 2543-2141
Online Access:Get full text
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Summary:Medical image segmentation is a challenging task especially in multimodality medical image analysis. In this paper, an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer (MFGWO-PCNN) is proposed for multimodality medical image segmentation. Specifically, a two-stage medical image segmentation method based on bionic algorithm is presented, including image fusion and image segmentation. The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region. In the stage of image segmentation, an improved PCNN model based on MFGWO is proposed, which can adaptively set the parameters of PCNN according to the features of the image. Two modalities of FLAIR and T1C brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm. The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.
ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-021-0049-4