摘要
为有效分割中药材显微图像的目标信息,提出了一种基于灰狼优化算法(Gray wolf optimization,GWO)的改进型脉冲耦合神经网络(Pulse coupled neural networks,PCNN)中药材显微图像自动分割方法。首先,从适应处理显微图像的角度出发对传统PCNN模型进行简化与改进;其次,在训练图像中提取香农熵值作为GWO的适应度函数来自适应调节PCNN关键参数——链接系数β,进而实现图像目标的最优分割;最后,将所提算法与聚类分割法、OTSU法、传统PCNN法进行了实验比较,并用骰子系数、体积重叠误差、相对体积、精确度和交并比等常用医学图像分割评判标准对4种处理方法做了客观评价。实验结果表明,所提方法能够实现图像的自适应分割,较好地保持了图像细节、纹理及边缘等信息,对不同显微图像分割准确度高,改善了图像的分割性能,具有较强的适用性。
In order to segment the target information of the microscopic image of Chinese herbal medicine(CHM)effectively,an improved Pulse Coupled Neural Networks(PCNN)automatic segmentation method of CHM microscopic image was proposed based on Gray Wolf Optimization(GWO)algorithm.Firstly,the traditional PCNN model was simplified and improved from the perspective of adapting to the processing of microscopic images;Secondly,Shannon entropy was extracted from the training image as the fitness function of GWO to adaptively adjust the key parameter(link coefficientβ)of PCNN,then the optimal segmentation of image target was realized;Finally,the proposed algorithm was compared with clustering segmentation,OTSU and traditional PCNN method,and the four processing methods were objectively evaluated by common medical image segmentation evaluation criteria such as Dice coefficient,volume overlap error,relative volume,accuracy and intersection union ratio.The experimental results showed that,the proposed method could realize the adaptive segmentation of image,better maintain the information of image detail,texture and edge,had high segmentation accuracy for different microscopic images,could improve the segmentation performance of image and had strong applicability.
作者
刘勍
黄金
张亚亚
赵利民
赵玉祥
LIU Qing;HUANG Jin;ZHANG Yaya;ZHAO Limin;ZHAO Yuxiang(School of Electronic Information and Electrical Engineering,Tianshui Normal University,Tianshui 741001,China)
出处
《信阳师范学院学报（自然科学版）》
CAS
2024年第1期120-126,共7页
Journal of Xinyang Normal University(Natural Science Edition)
基金
国家自然科学基金项目(61461046)
甘肃省自然科学基金项目(20JR10RA802,20JR5RA494)
甘肃省教育厅:教育揭榜挂帅项目(2021jyjbgs-06)
国家级大学生创新训练计划项目(S202110739002)
天水师范学院研究生创新引导项目(TYCX2036)。
