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基于形态特征和k均值聚类的黄斑检测与定位 被引量:4

Detecting and Locating the Macular Using Morphological Features and k-means Clustering
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摘要 彩色眼底图像已经广泛地应用于眼科相关疾病的辅助诊断和筛查。眼底图像中的黄斑区域检测和中心定位是眼科疾病分级、诊疗的重要步骤。提出一种有效检测与定位黄斑的方法,通过分析黄斑的低亮度和趋于圆形的形态特征,可以不依赖视盘和血管信息,在二值化眼底图像中实现黄斑检测,确定黄斑区域。改进k均值聚类方法,引入图像的空间信息,优化聚类对象,获取黄斑的边缘信息,实现黄斑中心的有效定位。在公开的眼底图像数据库上验证方法的性能,具有较高的准确率。对正常和存在病变的眼底图像的黄斑中心有效定位,可达到96.11%和92.12%,平均准确率达到93.92%。实验表明,提出的基于形态特征和k均值聚类的黄斑检测与定位方法简单、高效,对眼科疾病的计算机辅助诊断有实用价值。 Color fundus images have been widely used in the diagnosis and screening of ophthalmic diseases.The macular detection and foveal location in fundus images are important steps in grading and diagnosis of ophthalmic diseases. An efficient method not relying on the optic and vascular information was proposed in this work for detecting and locating macular foveal. After a general analysis on morphological characteristics of macular,which was low brightness and round,the area of macular could be ensured in the binary images.Then,an improved k-means clustering method was proposed on the basis of spatial information of images and optimizes clustering objects to obtain the edge information of macular and achieve accurate position of the macula foveal. Experimental tests showed good performance in the public fundus images database. For the normal and the pathological changes of the fundus images,the effective location rate of the macula was 96. 11%and 92. 12% respectively,and the average accuracy reached 93. 92%. Thus the proposed method based on morphological features and k-means clustering proved a simple,efficient and useful tool for computer-aided diagnosis of ocular diseases.
出处 《中国生物医学工程学报》 CAS CSCD 北大核心 2017年第6期654-660,共7页 Chinese Journal of Biomedical Engineering
基金 福建省中青年教师教育科研项目(JAT160398) 福建省高校自然基金青年重点项目(JZ160467) 福州市科技计划项目(2016-S-116)
关键词 黄斑检测 黄斑定位 形态特征 K均值聚类 macular detection macular location morphological feature k-means clustering
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  • 1Zhao-Dong Du,Li-Ting Hu,Gui-Qiu Zhao,Yan Ma,Zhan-Yu Zhou,and Tao Jiang.Epidemiological characteristics and risk factors of diabetic retinopathy in type 2 diabetes mellitus in Shandong Peninsula of China[J].International Journal of Ophthalmology(English edition),2011,4(2):202-206. 被引量:12
  • 2李天庆,张毅,刘志,胡东成.Snake模型综述[J].计算机工程,2005,31(9):1-3. 被引量:47
  • 3张惠蓉,刘宁朴,夏英杰,田力.糖尿病视网膜病变新生血管和视力预后[J].中华眼底病杂志,1995,11(2):71-73. 被引量:12
  • 4Abràmoff M D, Garvin M K, and Sonka M. Retinal imaging and image analysis[J]. IEEE Reviews in Biomedical Engineering, 2010, 3: 169-208.
  • 5Deepak K S and Sivaswamy J. Automatic assessment of macular edema from color retinal images[J]. IEEE Transactions on Medical Imaging, 2012, 31(3): 766-776.
  • 6Niemeijer M, Abràmoff M D, and Van Ginneken B. Fast detection of the optic disc and fovea in color fundus photographs[J]. Medical Image Analysis, 2009, 13(6): 859-870.
  • 7Sinthanayothin C, Boyce J F, Cook H L, et al.. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images[J]. British Journal of Ophthalmology, 1999, 83(8): 902-910.
  • 8Li H and Chutatape O. Automatic location of optic disk in retinal images[C]. Proceedings of IEEE International Conference on Image Processing, Thessaloniki, Greece, 2001, 2: 837-840.
  • 9Hoover A and Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels[J]. IEEE Transactions on Medical Imaging, 2003, 22(8): 951-958.
  • 10Abdel-Razik Youssif A A H, Ghalwash A Z, and Abdel-Rahman Ghoneim A A S. Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter[J]. IEEE Transactions on Medical Imaging, 2008, 27(1): 11-18.

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