基于模糊C均值聚类的比色传感器阵列图像分割算法
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摘要
结合当前比色传感器阵列多样性、不稳定等特点,并针对当前现有的阵列图像分割算法中或者效率低,或者易受光照环境影响等现状,本文在模糊C均值聚类算法基础上,提出了一种图像分割算法.该算法首先通过HSI颜色空间下I分量在行、列投影实现图像网格划分,并结合局部阵列点图像的平滑直方图信息解决了FCM算法聚类条件初始化的难题.其次,为了提高阵列点图像分割结果的准确度,该算法通过目标函数引入了不同权重系数的H分量和I分量,实现了色彩信息的引入.通过图像分割效果测试,本文所提出的图像分割算法在所有阵列点图像分割中展示了96.54%的总体最优分割精度,可以有效、准确地实现比色传感器阵列图像的目标提取.
Combining with the characteristics of current colorimetric sensor array such as diversity, instability, etc., and aiming at the current situation of the existing array image segmentation algorithm, such as low efficiency or susceptible to illumination environment, etc., this study proposes an image segmentation algorithm based on the fuzzy C-means clustering algorithm. Firstly, this algorithm achieves the grid division of image by the projection of I component in row and column under the HSI color space, and solves the problem of the initialization of clustering condition of FCM algorithm by combining with the smooth histogram information of local array point images. Secondly, in order to improve the accuracy of the result of segmentation of image points, the algorithm introduces the H component and I component of different weight coefficients through the objective function to realize the introduction of color information. Through the test of the effect of image segmentation, the image segmentation algorithm proposed in this study shows the overall optimal segmentation precision of 96.54% in all the image segmentation of the array points, and can effectively and accurately realize the target extraction of the colorimetric sensor array image.
Combining with the characteristics of current colorimetric sensor array such as diversity, instability, etc., and aiming at the current situation of the existing array image segmentation algorithm, such as low efficiency or susceptible to illumination environment, etc., this study proposes an image segmentation algorithm based on the fuzzy C-means clustering algorithm. Firstly, this algorithm achieves the grid division of image by the projection of I component in row and column under the HSI color space, and solves the problem of the initialization of clustering condition of FCM algorithm by combining with the smooth histogram information of local array point images. Secondly, in order to improve the accuracy of the result of segmentation of image points, the algorithm introduces the H component and I component of different weight coefficients through the objective function to realize the introduction of color information. Through the test of the effect of image segmentation, the image segmentation algorithm proposed in this study shows the overall optimal segmentation precision of 96.54% in all the image segmentation of the array points, and can effectively and accurately realize the target extraction of the colorimetric sensor array image.
引文
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11 Rajaby E,Ahadi SM, Aghaeinia H. Robust color image segmentation using fuzzy c-means with weighted hue and intensity. Digital Signal Processing, 2016, 51:170-183.[doi:10.1016/j.dsp.2016.01.010]
12 Zhao ZX, Cheng LZ, Cheng GQ. Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation.IET Image Processing, 2014, 8(3):150-161.[doi:10.1049/iet-ipr.2011.0128]
13 Shang RH, Tian PP, Jiao LC, et al. A spatial fuzzy clustering algorithm with kernel metric based on immune clone for SAR image segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016,9(4):1640-1652.[doi:10.1109/JSTARS.2016.2516014]
14 Mujica-Vargas D, Gallegos-Funes FJ, Rosales-Silva AJ. A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recognition Letters, 2013, 34(4):400-413.[doi:10.1016/j.patrec.2012.10.004]
15 Lopez-Molinero A, Linan D, Sipiera D, et al. Chemometric interpretation of digital image colorimetry. Application for titanium determination in plastics. Microchemical Journal,2010, 96(2):380-385.[doi:10.1016/j.microc.2010.06.013]
16 Wyszecki G, Stiles WS, Kelly KL. Color science:Concepts and methods, quantitative data and formulas. Physics Today,1968, 21(6):83.[doi:10.1063/1.3035025]
17 Rotaru C, Graf T, Zhang JW, Color image segmentation in HSI space for automotive applications. Journal of Real-Time Image Processing, 2008, 3(4):311-322.[doi:10.1007/s11554-008-0078-9]
18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.农业工程学报,2016, 32(8):142-147.
19 Deng N, Duan HL. An automatic and power spectra-based rotate correcting algorithm for microarray image.Proceedings of 27th Annual Conference of the IEEE Engineering in Medicine and Biology. Shanghai, China.2005. 898-901.
20 Siang Tan K, Mat Isa NA. Color image segmentation using histogram thresholding-Fuzzy C-means hybrid approach.Pattern Recognition, 2011,44(1):1-15.[doi:10.1016/j.patcog.2010.07.013]
21 Aptoula E, Lefevre S. On the morphological processing of hue. Image and Vision Computing, 2009, 27(9):1394-1401.[doi:10.1016/j.imavis.2008.12.007]
22 Cai WL, Chen SC, Zhang DQ. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 2007,40(3):825-838.[doi:10.1016/j.patcog.2006.07.011]
2 Hou CJ, Li JJ, Huo DQ, et al. A portable embedded toxic gas detection device based on a cross-responsive sensor array.Sensors and Actuators B:Chemical, 2012, 161(1):244-250.[doi:10.1016/j.snb.2011.10.026]
3贾明艳,冯亮.光化学比色传感器阵列的研究进展.分析化学,2013, 41(5):795-802.
4 Askim JR, Mahmoudi M, Suslick KS. Optical sensor arrays for chemical sensing:The optoelectronic nose. Chemical Society Reviews, 2013, 42(22):8649-8682.[doi:10.1039/c3cs60179j]
5 Capitan-Vallvey LF, Lopez-Ruiz N, Martinez-Olmos A, et al. Recent developments in computer vision-based analytical chemistry:A tutorial review. Analytica Chimica Acta,2015,899:23-56.[doi:10.1016/j.aca.2015.10.009]
6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.重庆大学学报,2012, 35(4):33-39.
7 Chen YD, Dougherty ER, Bittner ML. Ratio-based decisions and the quantitative analysis of cDNA microarray images.Journal of Biomedical Optics, 1997, 2(4):364-374.[doi:10.1117/12.281504]
8 Yang YH, Buckley MJ, Dudoit S, et al. Comparison of methods for image analysis on cDNA microarray data.Journal of Computational and Graphical Statistics, 2002,11(1):108-136.[doi:10.1198/106186002317375640]
9 Wang ZD,Zineddin B, Liang JL, et al. A novel neural network approach to cDNA microarray image segmentation.Computer Methods and Programs in Biomedicine, 2013,111(1):189-198.[doi:10.1016/j.cmpb.2013.03.013]
10 Bozinov D,Rahnenfuhrer J. Unsupervised technique for robust target separation and analysis of DNA microarrayspots through adaptive pixel clustering. Bioinformatics,2002, 18(5):747-756.[doi:10.1093/bioinformatics/18.5.747]
11 Rajaby E,Ahadi SM, Aghaeinia H. Robust color image segmentation using fuzzy c-means with weighted hue and intensity. Digital Signal Processing, 2016, 51:170-183.[doi:10.1016/j.dsp.2016.01.010]
12 Zhao ZX, Cheng LZ, Cheng GQ. Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation.IET Image Processing, 2014, 8(3):150-161.[doi:10.1049/iet-ipr.2011.0128]
13 Shang RH, Tian PP, Jiao LC, et al. A spatial fuzzy clustering algorithm with kernel metric based on immune clone for SAR image segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016,9(4):1640-1652.[doi:10.1109/JSTARS.2016.2516014]
14 Mujica-Vargas D, Gallegos-Funes FJ, Rosales-Silva AJ. A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recognition Letters, 2013, 34(4):400-413.[doi:10.1016/j.patrec.2012.10.004]
15 Lopez-Molinero A, Linan D, Sipiera D, et al. Chemometric interpretation of digital image colorimetry. Application for titanium determination in plastics. Microchemical Journal,2010, 96(2):380-385.[doi:10.1016/j.microc.2010.06.013]
16 Wyszecki G, Stiles WS, Kelly KL. Color science:Concepts and methods, quantitative data and formulas. Physics Today,1968, 21(6):83.[doi:10.1063/1.3035025]
17 Rotaru C, Graf T, Zhang JW, Color image segmentation in HSI space for automotive applications. Journal of Real-Time Image Processing, 2008, 3(4):311-322.[doi:10.1007/s11554-008-0078-9]
18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.农业工程学报,2016, 32(8):142-147.
19 Deng N, Duan HL. An automatic and power spectra-based rotate correcting algorithm for microarray image.Proceedings of 27th Annual Conference of the IEEE Engineering in Medicine and Biology. Shanghai, China.2005. 898-901.
20 Siang Tan K, Mat Isa NA. Color image segmentation using histogram thresholding-Fuzzy C-means hybrid approach.Pattern Recognition, 2011,44(1):1-15.[doi:10.1016/j.patcog.2010.07.013]
21 Aptoula E, Lefevre S. On the morphological processing of hue. Image and Vision Computing, 2009, 27(9):1394-1401.[doi:10.1016/j.imavis.2008.12.007]
22 Cai WL, Chen SC, Zhang DQ. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 2007,40(3):825-838.[doi:10.1016/j.patcog.2006.07.011]
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