Processing of the retinal cone image based on the MATLAB

doi:10.3969/j.issn.2095-4344.2013.15.018

Abstract

Abstract:

BACKGROUND: The cone cell for the image retina adaptive optical imaging system is characterized by the relatively concentrated gray distribution, vague window edge, the existing pseudo outline, and the adhesion of edge photograph. Looking for a suitable processing algorithm of the cone image to get the clear outline is the key content for the future work.
OBJECTIVE: To perform edge extraction of the retina cone cell image and get clear edge profile of the cone cell based on the MATLAB.
METHODS: According to the retinal image of the different parts of 30 normal subjects, based on Mat lab image processing toolbox, we could preprocess the cone cell image, edge extraction and process morphological, and get the clear edge profile of the cone cell image. The cell numbers of processed image was counted, the cone distribution characteristics were analyzed, and then the feasibility of this study which designed image processing algorithm applied to the study of the distribution characteristics of cone cells were verified.
RESULTS AND CONCLUSION: A clear outline of the retinal cones image was obtained successfully. From the results of cone cell’s density, as far away from the center fovea of macula, the cell density presented a trend of reduction; from the center fovea of macula partial range 0.5° to 1°, the retinal cones density decreased rapidly. The findings indicated that the algorithm of the image processing designed in this study can be used to study the retinal characteristics of the cones distribution.

Key words: tissue construction, cytology experiment in tissue construction, retina, cone cells, image processing, contrast stretching, edge extraction, Laplacian operator, adaptive optics, fovea, morphology, corrosion and expansion, National “863&rdquo, Program of China

CLC Number:

R318

Zhao Chao-yang, Yao Jun-ping, Liu Yong, Dai Yun, Gu Xin, Yin Zheng-qin. Processing of the retinal cone image based on the MATLAB[J]. Chinese Journal of Tissue Engineering Research, doi: 10.3969/j.issn.2095-4344.2013.15.018.

Figures/Tables 1

References

[1] Xiang D,Wang QL,Du QJ,et al. Bandaoti Guangdian. 2008; 29(1): 134-139.向东,王青玲,杜秋娇,等. 自适应光学技术获取高分辨率视网膜图像[J]. 半导体光电,2008,29(1):134-139.

[2] Zhang XL,Sun WT,Qiu SD,et al. Guoji Yanke Zazhi. 2005; 5(6):1239-1241.张小玲,孙文涛,邱曙东,等. 糖尿病性视网膜病变发病机制研究进展[J]. 国际眼科杂志,2005,5(6) :1239-1241.

[3] 陈宇,霍富荣,苗华. 对比度拉伸在目标探测与识别中的应用研究[C]. 焦作:第六届全国信息获取与处理学术会议论文集(2), 2008.

[4] Wu ZG,Wang YJ. Guangzi Xuebao. 2010;39(4):755-758.武志国,王延杰.一种基于直方图非线性变换的图像对比度增强方法[J].光子学报, 2010,39(4):755-758.

[5] Chen F,Yao JG,Yang YJ,et al. Jisuanji Gongcheng yu Yingyong. 2009;45(14): 167-169.陈芳,姚建刚,杨迎建,等. 灰度拉伸及边缘扫描在红外图像分割中的应用[J]. 计算机工程与应用, 2009,45(14): 167-169.

[6] Yao XW,Guo L,Zhao TY,et al. Xi’an Gongye Daxue Xuebao. 2012;32(5):367-372.姚西文,郭磊,赵天云,等.比较中值滤波快速算法及FPGA实现[J].西安工业大学学报, 2012,32(5):367-372.

[7] Hou FZ,Peng CW,Fang L. Wei Jisuanji Xinxi. 2011;27(1):69.侯法柱,彭楚武,方亮.图像中值滤波算法及其FPGA的实现[J].微计算机信息,2011,27(1):69.

[8] Qin A,Meng XL,Feng QJ,et al. Zhongguo Yiliao Shebei. 2010; 25(3):29-31.秦安,孟晓林,冯前进,等. 并行各向异性扩散算法与实时医学图像增强技术[J].中国医疗设备,2010,25(3):29-31.

[9] Liu C,Zhang D. Jisuanji Jishu yu Fazhan. 2006;16(8): 128-130.刘晨,张东. 边缘检测算子研究及其在医学图像中的应用[J].计算机技术与发展,2006,16(8):128-130.

[10] Jiang XC,Wan ZK,Chen L. Diannao Zhishi yu Jishu. 2006 (2): 138-141.江笑婵,万振凯,陈利.基于Matlab边缘提取的几种方法的比较[J].电脑知识与技术,2006(2):138-141.

[11] Zhou H,Li TM,Wei H. Beijing Shengwu Yixue Gongcheng. 2002;21(2):89-91.周浩,李天牧,尉洪.基于数字形态学的血液细胞图像边缘提取[J].北京生物医学工程,2002,21(2):89-91.

[12] Yang JW,Cheng TB,Zhong ZY,et al. Jisuanji Gongcheng yu Yingyong. 2011;47(36):177-179.杨健雯,程韬波,钟震宇,等.数字形态学和LoG算子结合的边缘检测技术[J].计算机工程与应用,2011,47(36):177-179.

[13] Zhou H,Li TM,Wei H. Beijing Shengwu Yixue Gongcheng. 2002;21(2):89-91.周浩,李天牧,尉洪.基于数字形态学的血液细胞图像边缘提取[J].北京生物医学工程,2002,21(2):89-91.

[14] Mistlberger A, Liebmann JM, Greenfield DS,et al. Heidelberg retina tomography and optical coherence tomography in normal, ocular-hypertensive, and glaucomatous eyes. Ophthalmology. 1999;106(10):2027-2032.

[15] Zeimer R, Shahidi M, Mori M,et al. A new method for rapid mapping of the retinal thickness at the posterior pole. Invest Ophthalmol Vis Sci. 1996;37(10):1994-2001.

[16] Hee MR, Izatt JA, Swanson EA,et al. Optical coherence tomography of the human retina. Arch Ophthalmol. 1995; 113(3):325-332.

[17] Zhao CY,Yao PJ,Dai Y,et al. Zhonghua Yan Shiguangxue yu Shijue Kexue Zazhi. 2011;13(6):419-422.赵超阳,姚军平,戴云,等. 自适应光学成像系统(MICRO/ZSC-1)活体测定正常人眼视锥细胞密度[J].中华眼视光学与视觉科学杂志,2011,13(6):419-422.

[18] Curcio CA, Sloan KR, Kalina RE,et al. Human photoreceptor topography. J Comp Neurol. 1990;292(4):497-523.

[19] Jiang WH. Ziran Zazhi. 2006;28(1):7-13.姜文汉.自适应光学技术[J]. 自然杂志,2006,28(1):7-13.

[20] Jonnal RS, Besecker JR, Derby JC,et al. Imaging outer segment renewal in living human cone photoreceptors. Opt Express. 2010;18(5):5257-5270.

[21] Kitaguchi Y, Fujikado T, Bessho K,et al. Adaptive optics fundus camera to examine localized changes in the photoreceptor layer of the fovea. Ophthalmology. 2008; 115(10):1771-1777.

[22] Chui TY, Song H, Burns SA. Individual variations in human cone photoreceptor packing density: variations with refractive error. Invest Ophthalmol Vis Sci. 2008;49(10):4679-4687.

[23] Jonas JB, Schneider U, Naumann GO. Count and density of human retinal photoreceptors. Graefes Arch Clin Exp Ophthalmol. 1992;230(6):505-510.

[24] Kitaguchi Y, Bessho K, Yamaguchi T,et al. In vivo measurements of cone photoreceptor spacing in myopic eyes from images obtained by an adaptive optics fundus camera. Jpn J Ophthalmol. 2007;51(6):456-461.

[25] Bessho K, Fujikado T, Mihashi T,et al. Photoreceptor images of normal eyes and of eyes with macular dystrophy obtained in vivo with an adaptive optics fundus camera. Jpn J Ophthalmol. 2008;52(5):380-385.