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.

医学图像分割是一项具有挑战性的任务，尤其是在多模态医学图像分析中。在本文中，提出了一种基于多混合特征灰狼优化器（MFGWO-PCNN）的改进脉冲耦合神经网络，用于多模态医学图像分割。具体而言，提出了一种基于仿生算法的两阶段医学图像分割方法，包括图像融合和图像分割。图像融合阶段利用基于最大能量区域的多模态医学图像融合模型融合来自不同模态的丰富信息。在图像分割阶段，提出了一种基于MFGWO的改进PCNN模型，可以根据图像的特征自适应地设置PCNN的参数。应用 FLAIR 和 T1C 脑 MRI 的两种模式来验证所提出的 MFGWO-PCNN 算法的有效性。实验结果表明，该方法在主观视觉和客观评价指标上均优于其他七种算法。