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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) |
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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.
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Received: 17 January 2018
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[1] Li Yan, Si Yang, Wang Xueqin, et al. Colorimetric sensor strips for lead (II) assay utilizing nanogold probes immobilized polyamide-6/nitrocellulose nano-fibers/nets[J]. Biosensors and Bioelectronics, 2013, 48(48C):244-250. [2] Kim HN, Ren Wenxiu, Kim JS, et al. Fluorescent and colorimetric sensors for detection of lead, cadmium, and mercury ions[J]. Chemical Society Reviews, 2012, 41(8):3210-3244. [3] Saha K, Agasti SS, Kim C, et al. Gold nanoparticles in chemical and biological sensing[J]. Chemical Reviews, 2012, 112(5):2739-2779. [4] Liu Ruili, Chen Zhaopeng, Wang Shasha, et al. Colorimetric sensing of copper(II) based on catalytic etching of gold nanoparticles[J]. Talanta, 2013, 112c(15):37-42. [5] Roy D, Fendler J. Reflection and absorption techniques for optical characterization of chemically assembled nanomaterials[J]. Advanced Materials, 2004, 16(6):479-508. [6] Ding Bin, Si Yang, Wang Xianfeng, et al. Label-free ultrasensitive colorimetric detection of copper(II) ions utilizing polyaniline/polyamide-6 nano-fiber/net sensor strips[J]. Journal of Materials Chemistry, 2011, 21(35):13345-13353. [7] Li Junjie, Wang Xianfeng, Huo Danqun, et al. Colorimetric measurement of Fe3+ using a functional paper-based sensor based on catalytic oxidation of gold nanoparticles[J]. Sensors and Actuators B: Chemical, 2017, 242:1264-1271. [8] Guo Jianfeng, Huo Danqun, Yang Mei, et al. Colorimetric detection of Cr (VI) based on the leaching of gold nanoparticles using a paper-based sensor[J]. Talanta, 2016, 161:819-825. [9] Capitán-Vallvey LF, López-Ruiz N, Martínez-Olmos A, et al. Recent developments in computer vision-based analytical chemistry: A tutorial review[J]. Analytica Chimica Acta, 2015, 899:23-56. [10] Vincent YJ. Critical issues in the processing of cDNA microarray images[D]. Blackburg: Virginia Polytechnic Institute and State University, 2001. [11] Park BS, Cho YS, Hong IS. Ratio-based decisions and the quantitative analysis of cDNA microarray images [J]. Journal of Biomedical Optics, 1997, 2(4):364-374. [12] Jr RH, Barrera J, Hashimoto RF, et al. Microarray gridding by mathematical morphology[C]// XIV Brazilian Symposium on Computer Graphics and Image Processing. Florianopolis: IEEE Computer Society, 2001:112. [13] Deng N, Duan H. An automatic and power spectra-based rotate correcting algorithm for microarray image[C]// 2005 International Conference of the IEEE-EMBS. Shanghai: IEEE, 2005:898-901. [14] Duggan DJ, Bittner M, Chen Y, et al. Expression profiling using cDNA microarrays [J]. Nature Genetics, 1999, 21(1):10-14. [15] Kim JH, Kim HY, Lee YS. A novel method using edge detection for signal extraction from cDNA microarray image analysis[J]. Experimental and Molecular Medicine, 2001, 33(2):83-88. [16] Antonio PG, Damiance J, Liang Z. A dynamical model with adaptive pixel moving for microarray images segmentation[J]. Real-Time Imaging, 2004, 10(4):189-195. [17] Bozinov D, Rahnenfuhrer J. Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering[J]. Bioinformatics. 2002, 18(5):747-756. [18] Yang YH, Buckley MJ, Dudoit S, et al. Comparison of methods for image analysis on cDNA microarray data[J]. Journal of Computational & Graphical Statistics, 2002, 11(1):108-136. [19] Wang Z, Zineddin B, Liang J, et al. A novel neural network approach to cDNA microarray image segmentation[J]. Computer Methods & Programs in Biomedicine, 2013, 111(1):189-198. [20] Rajaby E, Ahadi SM, Aghaeinia H. Robust color image segmentation using fuzzy c-means with weighted hue and intensity[J]. Digital Signal Processing, 2016, 51:170-183. [21] 艾大萍, 尹晓红, 刘伯强,等. 一种骨髓细胞识别分类算法的研究[J]. 中国生物医学工程学报, 2009, 28(4):549-553. [22] 高丽, 令晓明. 基于数学形态学的HSI空间彩色边缘检测方法[J]. 光电工程, 2010, 37(4):125-129. [23] Aptoula E, Vre S. On the morphological processing of hue[J]. Image & Vision Computing, 2009, 27(9):1394-1401. [24] Adams R, Bischof L. Seeded region growing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 36(6):641-647. [25] 柯尔挺, 厉力华, 刘伟,等. 基于视觉感知信息的乳腺钼靶肿块检测分析与自动提取[J]. 中国生物医学工程学报, 2014, 33(1):28-36. [26] Ito S, Yoshioka M, Omatu S, et al. An image segmentation method using histograms and the human characteristics of HSI color space for a scene image[J]. Artificial Life & Robotics, 2006, 10(1):6-10. [27] 罗小刚, 汪德暖, 侯长军,等. 卟啉传感阵列图像特征值自动提取方法[J]. 重庆大学学报, 2012, 35(4):33-39. [28] Tan K S, Isa N A M. Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach[J]. Pattern Recognition, 2011, 44(1):1-15. [29] 翟瑞芳, 方益杭, 林承达,等. 基于高斯HI颜色算法的大田油菜图像分割[J]. 农业工程学报, 2016, 32(8):142-147. [30] Rotaru C, Graf T, Zhang J. Color image segmentation in HSI space for automotive applications[J]. Journal of Real-Time Image Processing, 2008, 3(4):311-322. [31] Shaker F, Monadjemi SA, Naghsh-Nilchi AR. Automatic detection and segmentation of sperm head, acrosome and nucleus in microscopic images of human semen smears[J]. Computer Methods & Programs in Biomedicine, 2016, 132:11-20. [32] Ruiz-Ruiz G, Navas-Gracia LM. Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA)[J]. Computers & Electronics in Agriculture, 2009, 68(1):88-96. [33] Lagasse MK, Rankin JM, Askim JR, et al. Colorimetric sensor arrays: Interplay of geometry, substrate and immobilization[J]. Sensors & Actuators B Chemical, 2014, 197(197):116-122. [34] 李修霞, 荆林海, 李慧,等. 参考1维光谱差异的区域生长种子点选取方法[J]. 中国图象图形学报, 2016, 21(9):1256-1264. [35] Fan J, Zeng G, Body M, et al. Seeded region growing: an extensive and comparative study[J]. Pattern Recognition Letters, 2005, 26(8):1139-1156. [36] 杨家红, 刘杰, 钟坚成,等. 结合分水岭与自动种子区域生长的彩色图像分割算法[J]. 中国图象图形学报, 2010, 15(1):63-68. [37] Jeevakala S, Brintha TA, Rangasami R. A novel segmentation of cochlear nerve using region growing algorithm[J]. Biomedical Signal Processing & Control, 2018, 39:117-129. [38] Zhao Z, Cheng L, Cheng G. Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation[J]. IET Image Processing, 2014, 8(3):150-161. |
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