Automatic Extraction Method for Feature of Nanogold-Based Membranes Array Image
Yi Xin1 , Luo Xiaogang2#*, Qian Ye2 , Huo Danqun2# , Hou Changjun2#
1.(Dapartment of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China) 2.(College of Bioengineering, Chongqing University, Chongqing 400044, China)
Abstract：As a colorimetric sensor that is able to detect heavy metal ion, nanogold-based membranes have the advantages of high speed, accuracy and high specificity. Currently, processing image one by one manually is the main method used for extracting the sensor′s color features, which is inefficient, is easy to introduce human error, and the reproducibility of the acquired feature information is also poor. In order to solve these problems, here we proposed a two-stage automatic feature extraction method based on HSI color space and seeded region growing (SRG) on the basis of array image of the nanogold-based membrane. At the first stage, the array image was transformed from RGB space to HSI space to complete rough segmentation, spots-gridding and filtering; based on the first stage, the second stage proposed a seeded region growing (SRG) algorithm with similarity measurement based on hue attractive force (HAF) and intensity attractive force (IAF), which combined the adjacency characteristic of physical location of pixel of SRG algorithm and adjacency characteristic of color space of HAF and IAF to realize the precise segmentation and feature extraction effectively. In the experiment, 10 sheets of 5*5 paper-based arrays (250 reaction points in total) were used to test the accuracy and stability of this feature extraction method, and 10 sheets of paper-based arrays of different sizes were used to test the self-adaptation characteristics. The results of the experiment showed that the average error of the feature extraction method was less than 1%, maximum error and standard error was less than 5%, and the correct rate was 100% for arrays of different sizes. In conclusion, the proposed method was high in accuracy, stability, and adaptability.
作者简介: #中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)
引用本文:
易鑫, 罗小刚, 钱烨, 霍丹群, 侯长军. 纸基纳米金阵列图像特征值自动提取方法[J]. 中国生物医学工程学报, 2019, 38(2): 184-191.
Yi Xin, Luo Xiaogang, Qian Ye, Huo Danqun, Hou Changjun. Automatic Extraction Method for Feature of Nanogold-Based Membranes Array Image. Chinese Journal of Biomedical Engineering, 2019, 38(2): 184-191.
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