改进的快速FCM 及SVM 实现糖网白色病灶的自动检测

doi:10.3969/j.issn.0258-8021. 2013. 03.08

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Abstract To develop an automated diabetic retinopathy (DR) screening system, an automatically detecting approach based on improved and fast FCM (IFFCM) as well as SVM was established and studied. First, color fundus images were segmented by IFFCM, and candidate regions of bright lesions were obtained. The median filter was added to objective function of FCM and the result of Kmeans clustering was used to initialize clustering centers of FCM, so the new algorithm overcome the shortcomings of high complexity and sensitivity to noise. Second, a twolevel SVM classification structure was applied to classify the candidate regions. The bright lesions were picked up by features of candidate regions in stage one. Another group of features were used to discriminate hard exudates from cotton wool spots in stage two; as a result the automated detection of bright lesions in fundus images was accomplished. The approach was tested on a new set of 65 fundus images. With an imagebased criterion, sensitivity of 100%, specificity of 950% and accuracy of 9846% are achieved. Average sensitivity of 9642%/9715% and average positive predict value of 9003%/9118% are also achieved with a lesionbased criterion (hard exudates/cotton wool spots). Furthermore, the average time cost in processing an image is 3556 seconds. Results suggest that the combination of the good result of coarse segmentation provided by IFFCM and higher recognition rate of SVM makes the results of automated detection better. It means that the proposed approach can efficiently detect bright lesions of DR from fundus images.

Key words： fundus images diabetic retinopathy fuzzy C-means（FCM） support vector machine (SVM) automated detection

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	GAO Wei Wei¹SHEN Jian Xin^1*WANG Yu Liang¹LIANG Chun¹ZUO Jing²

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GAO Wei Wei¹SHEN Jian Xin^1*WANG Yu Liang¹LIANG Chun¹ZUO Jing². Automated Detection of Bright Lesions of Diabetic Retinopathy Based on Improved and Fast FCM and SVM[J]. journal1, 2013, 32(3): 305-312.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2013. 03.08 OR http://cjbme.csbme.org/EN/Y2013/V32/I3/305

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