基于动态突触神经网络的菌落图像轮廓提取方法  

Contour Extraction Method of Colony Images Based on Dynamic Synaptic Neural Network

作　　者：蔡哲飞 范影乐 武薇 Cai Zhefei;Fan Yingle;Wu Wei(Laboratory of Pattern Recognition and Image Processing,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学模式识别与图像处理实验室,杭州310018

出　　处：《中国生物医学工程学报》2022年第6期699-707,共9页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学基金(61501154)。

摘　　要：菌落图像轮廓提取的准确性对于微生物菌落形态学和特征分析具有重要意义。本研究构建一种基于动态突触神经网络的菌落图像轮廓提取方法。首先面向视网膜光电转换过程,提出一种基于光照强度的感受野尺度自适应调节模型;其次构建融合了电突触与化学突触侧向调节的带泄漏积分触发(LIF)模型,通过神经元膜电位、空间分布关系以及响应时间差调节电突触和化学突触强度,并获得有无侧向调节作用下的菌落边缘敏感图像;最后比较有无侧向调节作用下神经元响应时间差,利用STDP学习规则对突触权重进行动态更新,以此调整菌落轮廓细节,并将其与初级轮廓响应相结合,得到最终菌落图像轮廓信息。以实验室所采集的40幅菌落图像为研究对象,选取边缘置信度BIdx、平均结构相似度MSSIM,以及综合性能EIdx为评价指标。结果表明,本方法所获取的菌落轮廓更加准确、连续且噪声少,BIdx和MSSIM分别为0.6514±0.0565和0.8318±0.0261,EIdx为0.7657±0.0274,较OS、BAR和LS等3种基于生物视觉的对比方法分别有显著性提升(P<0.01)。所构建的动态突触神经网络适用于菌落等具有丰富细节特征的图像轮廓提取,可为融入生物视觉机制的神经计算模型研究和应用提供一种新思路。The accuracy of colony image contour extraction is of great significance for microbial colony morphology and feature analysis.Based on the dynamic synaptic neural network,we constructed an image contour extraction method for colonies.Firstly,simulating the photoelectric conversion process of retinal,proposed an adaptive adjustment model of receptive field scale based on light intensity.Secondly,a LIF model combining the lateral regulation of electrical synapse and chemical synapse was constructed.Using neuron membrane potential,spatial distribution relationship and response time difference to adjust the strength of electrical synapse and chemical synapse,the colony edge sensitive image with or without lateral regulation was obtained.Finally,compared the difference of neurons response time with or without lateral regulation,and using STDP rules to dynamically update the weight of neuronal synaptic,so as to adjust the contour details of colony images.The adjusted contour response with the primary contour response were combined to obtain the final contour information of colony images.Taking 40 colony images collected in the laboratory as research objects,and selecting edge confidence BIdx,average reconstructed similarity MSSIM and comprehensive index EIdx as the evaluation indexes.The results showed that the contours obtained by this study was more accurate,continuous and has less noise.BIdx and MSSIM were 0.6514±0.0565 and 0.8318±0.0261,respectively.Meanwhile,EIdx was 0.7657±0.0274,which was significantly higher(P<0.01)than that of biological vision based methods of OS,BAR and LS.The dynamic synaptic neural network constructed in this paper is suitable for image contour extraction with rich detailed features such as colonies and may provide a new way for the research and application of neural computing models that integrate the biological vision mechanism.

关 键 词：菌落识别 动态突触 轮廓提取 神经计算模型 

分 类 号：R318[医药卫生—生物医学工程]