免散瞳眼底图像中微动脉瘤的高效自动检测

doi:10.3969/j.issn.0258-8021.2012.06.006

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摘要为快速、有效地自动检测免散瞳眼底图像中的微动脉瘤，构建基于免散瞳眼底图像的糖尿病视网膜病变自动筛查系统，提出了一种简单而高效的微动脉瘤自动检测算法。在对免散瞳眼底图像G通道预处理的基础上，利用数学形态学分割提取硬性渗出和血管；并通过将二者从扩展极小值变换后的二值图像中去除而获得微动脉瘤候选区域；进而根据尺寸信息获取真正的微动脉瘤。利用该算法对两组不同质量免散瞳眼底图像进行微动脉瘤自动检测，并对检测结果进行统计分析。结果表明：两组图像检测结果精度均较高，相应指标间的相对误差均低于4%，且处理效率高(平均一幅图像的处理时间为9.7 s)。该算法能够高效地自动检测出免散瞳眼底图像中的微动脉瘤，且算法稳定可靠，具有很高的实用价值。

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	高玮玮¹沈建新^1* 王玉亮^1梁春^1左晶^2

关键词 ：免散瞳眼底图像, 微动脉瘤, 数学形态学, 扩展极小值变换, 自动检测

Abstract：In order to automatically detect microaneurysms from non-dilated fundus images, and develop an automated diabetic retinopathy screening system, a novel, simple and efficient algorithm of automatic microaneurysms detection was established and studied in this work. Hard exudates and vessel were segmented by mathematical morphology on the basis of preprocessed green channel of the original non-dilated fundus image in RGB channels. Then, candidate regions of microaneurysms were obtained by removing hard exudates and vessel from the resulting image of extended-minima transform on the previously preprocessed image. Thus, the true lesions of microaneurysms were separated based on size information. The algorithm was tested on two groups of non-dilated fundus images with different quality. Statistical analysis of detection results showed that precision for the two groups were both high, each relative error of corresponding indexes between two groups was lower than 4%, and processing efficiency was high for which the mean time cost for processing an image is 9.7 seconds. Results suggest that the algorithm can efficiently detect microaneurysms from non-dilated fundus images, and it is stable and reliable. As a result, the proposed algorithm has high practical value.

Key words： non-dilated fundus images microaneurysms mathematical morphology extended-minima transform automated detection

基金资助: 国家高技术研究发展计划(863计划) (2006AA020804)；中央高校基本科研业务费专项（南航NJ20120007）；江苏省科技支撑计划(BE2010652)；江苏省普通高校研究生科研创新计划项目（CXLX11_0218）

引用本文:

高玮玮¹沈建新^1* 王玉亮^1梁春^1左晶^2
. 免散瞳眼底图像中微动脉瘤的高效自动检测[J]. 中国生物医学工程学报, 2012, 31(6): 839-845.
GAO WeiWei¹SHEN JianXin^1*WANG YuLiang¹LIANG Chun^1ZUO Jing²
. Efficient and Automated Detection of Microaneurysms from Non-Dilated Fundus Images. journal1, 2012, 31(6): 839-845.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2012.06.006 或 http://cjbme.csbme.org/CN/Y2012/V31/I6/839

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