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 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.

Automated summary

An improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer (MFGWO-PCNN) is proposed for multimodality medical image segmentation, achieving better results compared to other algorithms through a two-stage medical image segmentation method involving image fusion and segmentation.