Abstract：To meet the requirement of intelligent morphology classification of compound colonies, a convolution neural network for colony classification was proposed in this work. All connected domains in the culture dish were obtained by level set evolution. Limit corrosion was used to determine the connected domains that seed points were greater than one. Obtaining the convex closures of the connected domains, the concave points were detected. By connecting the corresponding concave points, the connected domains with adhesion were segmented. Herein 600 single colony samples were normalized, and the data were expanded to 30,000 cases by rotating and flipping and superimposing random noise with signal-to-noise ratio not exceeding 5%. Samples of 70% were used as the training set for the network. The network was cross-verified by 10 folds. Samples of 30% were used as the test set. The weighted average accuracy of the four colonies reached 87.50%, of which the classification accuracy of spotted smooth colonies was 86.40%, that of circle like wavy colonies was 87.21%, that of oval colonies was 88.11%, and that of irregular other colonies was 87.25%. Compute unified device architecture (CUDA) was adopted to accelerate the algorithm. The running time was optimized to 1/10 of the original time. In terms of running speed and convenience, the algorithm far exceeded the traditional methods. The statistical results showed that the proposed method could effectively complete the task of intelligent classification and identification of compound colonies. This method showed good expansibility and self-learning function, which had reference value for intelligent analysis of biochemical samples based on images.
余辉, 杜培培, 刘祥, 刘政, 朱险峰, 曹玉珍. 基于卷积神经网络的复合菌落智能分类识别[J]. 中国生物医学工程学报, 2020, 39(1): 26-32.
Yu Hui, Du Peipei, Liu Xiang, Liu Zheng, Zhu Xianfeng, Cao Yuzhen. Intelligent Classification and Identification of Composite Colonies Based on Convolutional Neural Network. Chinese Journal of Biomedical Engineering, 2020, 39(1): 26-32.
[1] 陈可意. 菌落计数与分类智能算法研究 [D]. 天津:天津大学,2014. [2] 李凤梅. 食品微生物检验 [M]. 北京:化学工业出版社, 2015:15-16. [3] GB4789.2—2016食品安全国家标准 [S].北京:中国标准出版社,2017. [4] 司秀娟,王红强,宋良图. 基于图像处理的菌落自动计数系统 [J]. 北京生物医学工程, 2017, 36(6): 633-638. [5] 武宗茜,王鹏,丁天怀. 活动轮廓模型在重叠藻细胞计数中的应用 [J]. 计算机工程, 2012, 38(3): 209-211. [6] Dorbe N, Jaundalders A, Kadikis R, et al. FCN and LSTM based computer vision system for recognition of vehicle type, license plate number, and registration country [J]. Automatic Control and Computer Sciences, 2018, 52(2): 146-154. [7] 杨真真,匡楠,范露,等. 基于卷积神经网络的图像分类算法综述 [J]. 信号处理, 2018, 34(12): 1474-1489. [8] Kukharenko A I, Konushin A S. Simultaneous classification of several features of a person′s appearance using a deep convolutional neural network [J]. Pattern Recognition and Image Analysis, 2015, 25(3): 461-465. [9] Ferrari A, Lombardi S, Signoroni A. Bacterial colony counting with convolutional neural networks in digital microbiology imaging [J]. Pattern Recognition, 2017, 61: 629-640. [10] 张力新,张黎明,杜培培,等. 基于改进水平集的菌落图像智能计数算法 [J]. 天津大学学报(自然科学与工程技术版), 2019, 52(1): 84-89. [11] 王平,陶德威,熊嵩,等. 基于凹点匹配的黏连车辆图像分割方法 [J]. 南昌大学学报(理科版), 2017, 41(5): 433-436. [12] Sun Yi, Wang Xiaogang, Tang Xiaoou. Deep learning face representation from predicting 10,000 classes [C] //IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus: IEEE Computer Society, 2014: 1891-1898. [13] Lécun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. [14] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [C] //International Conference on Neural Information Processing Systems. Carson City城市名称: Nips Foundation, 2012: 1097-1105. [15] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition [C] //Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778. [16] Mnih A, Kavukcuoglu K. Learning word embeddings efficiently with noise-contrastive estimation [M] // Advances in Neural Information Processing Systems. New York: Curran Associates, Inc, 2013: 2265-2273. [17] Duchi J, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization [J]. Journal of Machine Learning Research, 2011, 12(7): 257-269. [18] Acharya U R, Fujita H, Oh S L, et al. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals [J]. Applied Intelligence, 2019, 49(1): 16-27. [19] Pontius RG, Millones M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment [J]. International Journal of Remote Sensing, 2011, 32(15): 4407-4429. [20] Abadi M, Barham P, Chen J, et al. TensorFlow: a system for large-scale machine learning [C] //Proceeding of the 12th USENIX Conference on Operating System Design and Implementation. Savannah: USENIX Association, 2016, 16: 265-283. [21] 孟晓林,秦安,徐建,等. 基于CUDA的快速三维医学图像分割 [J]. 中国医学物理学杂志, 2010, 27(2): 1716-1720. [22] 余伟,彭宽宽,陈伟,等. 基于高光谱技术的培养基上细菌菌落分类方法研究 [J]. 分析化学, 2016, 44(8): 1221-1226.
