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视网膜功能启发的边缘检测层级模型

郑程驰 范影乐

郑程驰, 范影乐. 视网膜功能启发的边缘检测层级模型. 自动化学报, 2023, 49(8): 1771−1784 doi: 10.16383/j.aas.c220574
引用本文: 郑程驰, 范影乐. 视网膜功能启发的边缘检测层级模型. 自动化学报, 2023, 49(8): 1771−1784 doi: 10.16383/j.aas.c220574
Zheng Cheng-Chi, Fan Ying-Le. Multi-layer edge detection model inspired by retinal function. Acta Automatica Sinica, 2023, 49(8): 1771−1784 doi: 10.16383/j.aas.c220574
Citation: Zheng Cheng-Chi, Fan Ying-Le. Multi-layer edge detection model inspired by retinal function. Acta Automatica Sinica, 2023, 49(8): 1771−1784 doi: 10.16383/j.aas.c220574

视网膜功能启发的边缘检测层级模型

doi: 10.16383/j.aas.c220574
基金项目: 国家自然科学基金(61501154)资助
详细信息
    作者简介:

    郑程驰:杭州电子科技大学硕士研究生. 2020年获得华北水利水电大学学士学位. 主要研究方向为模式识别, 图像处理.E-mail: 201060093@hdu.edu.cn

    范影乐:杭州电子科技大学教授. 2001年获浙江大学博士学位. 主要研究方向为神经信息学, 机器视觉和机器学习. 本文通信作者.E-mail: fan@hdu.edu.cn

Multi-layer Edge Detection Model Inspired by Retinal Function

Funds: Supported by National Natural Science Foundation of China (61501154)
More Information
    Author Bio:

    ZHENG Cheng-Chi Master student at Hangzhou Dianzi University. He received his bachelor degree from North China University of Water Resources and Electric Power in 2020. His research interest covers pattern recognition and image processing

    FAN Ying-Le Professor at Hangzhou Dianzi University. He received his Ph.D. degree from Zhejiang University in 2001. His research interest covers neuroinformatics, machine vision, and machine learning. Corresponding author of this paper

  • 摘要: 基于视网膜对视觉信息的处理方式, 提出一种视网膜功能启发的边缘检测层级模型. 针对视网膜神经元在周期性光刺激下产生适应的特性, 构建具有自适应阈值的Izhikevich神经元模型; 模拟光感受器中视锥细胞、视杆细胞对亮度的感知能力, 构建亮度感知编码层; 引入双极细胞对给光−撤光刺激的分离能力, 并结合神经节细胞对运动方向敏感的特性, 构建双通路边缘提取层; 另外根据神经节细胞神经元在多特征调控下延迟激活的现象, 构建具有脉冲延时特性的纹理抑制层; 最后将双通路边缘提取的结果与延时抑制量相融合, 得到最终边缘检测结果. 以150张来自实验室采集和AGAR数据集中的菌落图像为实验对象对所提方法进行验证, 检测结果的重建图像相似度、边缘置信度、边缘连续性和综合指标分别达到0.9629、0.3111、0.9159和0.7870, 表明所提方法能更有效地进行边缘定位、抑制冗余纹理、保持主体边缘完整性. 本文面向边缘检测任务, 构建了模拟视网膜对视觉信息处理方式的边缘检测模型, 也为后续构建由视觉机制启发的图像计算模型提供了新思路.
  • 图  1  边缘检测算法原理图

    Fig.  1  Principle of edge detection algorithm

    图  2  改进前后的Izhikevich模型对图像进行脉冲发放的结果对比图

    Fig.  2  Comparison of the image processing results of the Izhikevich model before and after improvement

    图  3  不同方式对存在弱边缘的菌落图像的处理结果

    Fig.  3  Different ways to process the image of colonies with weak edges

    图  4  边缘检测算法流程图

    Fig.  4  The procedure of edge detection algorithm

    图  5  Colony1 ~ Colony5的边缘检测结果(第1行为原图; 第2行为Canny检测的结果; 第3行为SPM检测的结果; 第4行为ISM检测的结果; 第5行为OSM检测的结果; 第6行为NLC检测的结果; 第7行为本文方法检测的结果)

    Fig.  5  Edge detection results of Colony1 to Colony5 (The first line is original images; The second line is the results of Canny; The third line is the results of SPM; The fourth line is the results of ISM; The fifth line is the results of OSM; The sixth line is the results of NLC; The seventh line is the results of the proposed method)

    图  6  Colony1 ~ Colony5的重建结果 (第1行为原图; 第 2 行为 Canny 检测结果的重建; 第 3 行为 SPM 检测结果的重建; 第 4 行为 ISM 检测结果的重建; 第 5 行为 OSM 检测结果的重建; 第 6 行为 NLC 检测结果的重建; 第 7 行为本文方法检测结果的重建)

    Fig.  6  Reconstruction results of Colony1 to Colony5 (The first line is original images; The second line is the results of Canny; The third line is the results of SPM; The fourth line is the results of ISM; The fifth line is the results of OSM; The sixth line is the results of NLC; The seventh line is the results of the proposed method)

    图  7  存在问题的案例

    Fig.  7  Cases with problems

    表  1  不同检测方法下的重建相似度MSSIM

    Table  1  MSSIM of different methods

    Serial numberMSSIM
    CannySPMISMOSMNLC本文方法
    Colony10.74520.77250.83570.92650.91750.9371
    Colony20.79510.79710.84900.95280.94470.9725
    Colony30.85760.86620.83140.91490.83370.9278
    Colony40.96900.98270.98380.98870.98930.9972
    Colony50.96340.97580.97800.97710.98830.9933
    下载: 导出CSV

    表  5  不同检测方法下的各评价指标的均值和标准差

    Table  5  Average and standard deviation of indexes of different methods

    CannySPMISMOSMNLC本文方法
    MSSIM0.8119±0.06490.8435±0.08630.8692±0.07780.9422±0.02910.9184±0.06090.9629±0.0335
    BIdx0.2091±0.15420.1724±0.20560.1702±0.10230.2763±0.14010.2940±0.22210.3111±0.1604
    CIdx0.7972±0.02790.8101±0.11250.8205±0.05610.8397±0.20500.9149±0.08570.9359±0.0306
    EIdx0.6568±0.05620.6697±0.05480.6801±0.05130.7341±0.02810.7211±0.08600.7870±0.0437
    下载: 导出CSV

    表  2  不同检测方法下的边缘置信度BIdx

    Table  2  BIdx of different methods

    Serial numberBIdx
    CannySPMISMOSMNLC本文方法
    Colony10.49880.46180.43070.58010.50580.6026
    Colony20.18210.15370.15530.33650.46150.4479
    Colony30.19830.15100.16100.26340.12630.3257
    Colony40.16310.14880.19060.14370.15210.2016
    Colony50.16200.18960.19020.18820.17350.1654
    下载: 导出CSV

    表  3  不同检测方法下的边缘连续性CIdx

    Table  3  CIdx of different methods

    Serial numberCIdx
    CannySPMISMOSMNLC本文方法
    Colony10.83770.85300.86010.86760.97490.9652
    Colony20.80690.86550.85330.82930.91770.9518
    Colony30.80640.74080.72930.82690.77640.9406
    Colony40.81430.86110.90440.84300.90150.9776
    Colony50.90470.84480.86320.85920.87090.9571
    下载: 导出CSV

    表  4  不同检测方法下的综合评价指标EIdx

    Table  4  EIdx of different methods

    Serial numberEIdx
    CannySPMISMOSMNLC本文方法
    Colony10.70470.71220.73440.81730.82190.8473
    Colony20.71910.65080.67220.76330.82440.8291
    Colony30.60480.65430.63630.72580.64020.7784
    Colony40.72870.74320.76490.73630.74540.7690
    Colony50.72310.74540.75120.74430.72620.7737
    下载: 导出CSV
  • [1] Gong X Y, Su H, Xu D, Zhang Z T, Shen F, Yang H B. An overview of contour detection approaches. International Journal of Automation and Computing, 2018, 15(6): 656-672 doi: 10.1007/s11633-018-1117-z
    [2] Yedjour H, Meftah B, Lézoray O, Benyettou A. Edge detection based on Hodgkin-Huxley neuron model simulation. Cognitive Processing, 2017, 18(3): 315-323 doi: 10.1007/s10339-017-0803-z
    [3] 罗佳骏, 武薇, 范影乐, 高云圆. 基于视觉感光层功能的菌落图像多强度边缘检测研究. 中国生物医学工程学报, 2014, 33(6): 677-686

    Luo Jia-Jun, Wu Wei, Fan Ying-Le, Gao Yun-Yuan. Multi-intensity edge detection for colony images based on the function of photoreceptor in visual system. Chinese Journal of Biomedical Engineering, 2014, 33(6): 677-686
    [4] 方芳, 范影乐, 廖进文, 张梦楠. 基于神经元突触可塑性机制图像边缘检测方法. 华中科技大学学报(自然科学版), 2015, (S1): 200−202, 206

    Fang Fang, Fan Ying-Le, Liao Jin-Wen, Zhang Meng-Nan. Image edge detection method based on synaptic plasticity mechanism. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2015, (S1): 200−202, 206
    [5] Fang T, Yuan J T, Yin R, Wu C L. A novel image edge detection method based on the asymmetric STDP mechanism of the visual path. Wireless Communications and Mobile Computing, 2022
    [6] Wang B, Chen L L, Zhang Z Y. A novel method on the edge detection of infrared image. Optik, 2019, 180: 610-614 doi: 10.1016/j.ijleo.2018.11.113
    [7] Chaoui C N, Ghomari A, Meftah B. Edge and anomaly detection of brain magnetic resonance images in a distributed environment. International Journal of Imaging Systems and Technology, 2022, 32(2): 642-657 doi: 10.1002/ima.22647
    [8] Ocko S A, Lindsey J, Ganguli S, Deny S. The emergence of multiple retinal cell types through efficient coding of natural movies. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montreal, Canada: Curran Associates Inc., 2018. 9411−9422
    [9] Maheswaranathan N, McIntosh L T, Tanaka H, Grant S, Kastner D B, Melander J B, et al. The dynamic neural code of the retina for natural scenes [Online], available: https://www.biorxiv.org/content/10.1101/340943v5, January 31, 2023
    [10] Izhikevich E M. Simple model of spiking neurons. IEEE Transactions on Neural Networks, 2003, 14(6): 1569-1572 doi: 10.1109/TNN.2003.820440
    [11] Ruda K, Rudzite A M, Field G D. The functional organization of retinal ganglion cell receptive fields across light levels [Online], available: https://www.biorxiv.org/content/10.1101/2022.09.15.508164v1, January 31, 2023
    [12] Ding W, Xiao L, Jing W, Zhang P M, Liang P J. Population activity changes during a trial-to-trial adaptation of bullfrog retinal ganglion cells. Neuroreport, 2014, 25(10): 801-805 doi: 10.1097/WNR.0000000000000191
    [13] Hübner C, Schütz A C. Rapid visual adaptation persists across saccades. iScience, 2021, 24(9): Article No. 102986
    [14] Li Y C, Cohen E D, Qian H H. Rod and cone coupling modulates photopic ERG responses in the mouse retina. Frontiers in Cellular Neuroscience, 2020, 14: Article No. 566712
    [15] Szatko K P, Korympidou M M, Ran Y L, Berens P, Dalkara D, Schubert T, et al. Neural circuits in the mouse retina support color vision in the upper visual field. Nature Communications, 2020, 11: Article No. 3481
    [16] Grimes W N, Songco-Aguas A, Rieke F. Parallel processing of rod and cone signals: Retinal function and human perception. Annual Review of Vision Science, 2018, 4: 123-141 doi: 10.1146/annurev-vision-091517-034055
    [17] Rucci M, Ahissar E, Burr D. Temporal coding of visual space. Trends in Cognitive Sciences, 2018, 22(10): 883-895 doi: 10.1016/j.tics.2018.07.009
    [18] Huang X L, Rangel M, Briggman K L, Wei W. Neural mechanisms of contextual modulation in the retinal direction selective circuit. Nature Communications, 2019, 10: Article No. 2431
    [19] Solomon S G. Retinal ganglion cells and the magnocellular, parvocellular, and koniocellular subcortical visual pathways from the eye to the brain. Elsevier, 2021, 178: 31-50
    [20] Sugita Y, Miura K, Furukawa T. Retinal ON and OFF pathways contribute to initial optokinetic responses with different temporal characteristics. European Journal of Neuroscience, 2020, 52(4): 3160-3165 doi: 10.1111/ejn.14697
    [21] Kerschensteiner D. Feature detection by retinal ganglion cells. Annual Review of Vision Science, 2022, 8: 135-169 doi: 10.1146/annurev-vision-100419-112009
    [22] Jacoby J, Schwartz G W. Typology and circuitry of suppressed-by-contrast retinal ganglion cells. Frontiers in Cellular Neuroscience, 2018, 12: Article No. 00269
    [23] Iacaruso M F, Gasler I T, Hofer S B. Synaptic organization of visual space in primary visual cortex. Nature, 2017, 547(7664): 449-452 doi: 10.1038/nature23019
    [24] 廖进文, 范影乐, 武薇, 高云圆, 李轶. 基于抑制性突触多层神经元群放电编码的图像边缘检测. 中国生物医学工程学报, 2014, 33(5): 513-524 doi: 10.3969/j.issn.0258-8021.2014.05.01

    Liao Jin-Wen, Fan Ying-Le, Wu Wei, Gao Yun-yuan, Li Yi. Image edge detection based on spike coding of multilayer neuronal population with inhibitory synapse. Chinese Journal of Biomedical Engineering, 2014, 33(5): 513-524 doi: 10.3969/j.issn.0258-8021.2014.05.01
    [25] 王典, 范影乐, 张梦楠, 武薇. 基于突触连接视通路方向敏感的图像分级边缘检测方法. 中国生物医学工程学报, 2015, 34(5): 522-532

    Wang Dian, Fan Ying-Le, Zhang Meng-Nan, Wu Wei. A hierarchical image edge detection method based on orientation sensitivity of visual pathway with synaptic connections. Chinese Journal of Biomedical Engineering, 2015, 34(5): 522-532
    [26] Majchrowska S, Pawlowski J, Gula G, Bonus T, Hanas A, Loch A, et al. AGAR a microbial colony dataset for deep learning detection. arXiv preprint arXiv: 2108.01234, 2021.
    [27] Govindarajan B, Panetta K A, Agaian S. Image reconstruction for quality assessment of edge detectors. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Singapore: IEEE, 2008. 691−696
    [28] Meer P, Georgescu B. Edge detection with embedded confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(12): 1351-1365 doi: 10.1109/34.977560
    [29] 磨少清. 边缘检测及其评价方法的研究 [博士学位论文], 天津大学, 中国, 2011

    Mo Shao-Qing. Research on Edge Detection and Its Evaluation [Ph.D. dissertation], Tianjin University, China, 2011
    [30] Niell C M, Scanziani M. How cortical circuits implement cortical computations: Mouse visual cortex as a model. Annual Review of Neuroscience, 2021, 44: 517-546 doi: 10.1146/annurev-neuro-102320-085825
    [31] Huang X L, Kim A J, Ledesma H A, Ding J, Smith R G, Wei W. Visual stimulation induces distinct forms of sensitization of On-Off direction-selective ganglion cell responses in the dorsal and ventral retina. Journal of Neuroscience, 2022, 42(22): 4449-4469 doi: 10.1523/JNEUROSCI.1391-21.2022
    [32] Kupers E R, Benson N C, Carrasco M, Winawer J. Asymmetries around the visual field: From retina to cortex to behavior. PLoS Computational Biology, 2022, 18(1): Article No. e1009771
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  • 收稿日期:  2022-07-14
  • 录用日期:  2022-11-29
  • 网络出版日期:  2023-03-09
  • 刊出日期:  2023-08-21

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