基于三维脉冲耦合神经网络模型的医学图像分割信号与信息处理

Segmentation of Medical Images Based on Three Dimensional Pulse Coupled Neural Network Model

该文将脉冲耦合神经网络模型从二维平面扩展到三维空间,同时提出一种新的乘积型互信息算法,将其作为脉冲耦合神经网络分割算法的最优分割准则,并将两者结合实现三维医学图像的整体自动分割.利用该文提出的算法对三维CT肺部图像进行分割实验,结果表明,该算法在保证分割精度的基础上显著地减少了分割运行时间,提高了分割效率,具有应用于医学图像分割的潜在价值.

In this study,the 2D pulse coupled neural network（PCNN） model is extended to the 3D space,and a new rule for optimal image segmentation,named product mutual information（PMI）,is proposed.Based on the 3D PCNN and PMI,an automatic segmentation algorithm is developed for 3D medical image segmentation. Three-dimensional CT lung images are segmented with the proposed method,showing reduced execution time and improved computation efficiency with high segmentation accuracy.The method is potentially useful for medical image segmentation.