多算法融合的眼底图像渗出液分割

doi:10.16180/j.cnki.issn1007-7820.2020.11.005

Abstract

Abstract:

Based on the luminance and edge characteristics of the exudates in fundus images, a multi-algorithm fusion method for automatic detection of the exudate is adopted in this paper to solve the problems of low sensitivity of the traditional algorithm and interference of dark lesions such as optic disc and other microangiomas in the detection results. In order to improve the segmentation efficiency and accuracy, this study uses top-hat and boottom-hat to enhance the image contrast of the original image, and then a dual threshold segmentation method combining genetic algorithm and optimal histogram entropy method is proposed to preliminarily segment the image. The experimental results show that the sensitivity and PPV of the algorithm are 83.6% and 93.2% at the pixel level, and the SE, specificity and accuracy are 95.2%, 86.2% and 90.8% respectively at the image level. The results obtained by testing on another independent DIARETDB1 database are 82.4%, 93.3%, 93.6%, 96.2%, 89.9%. Compared with other algorithms, this method can distinguish the exudates from other dark lesions, and the detection time is short, accurate and efficient.

Key words: diabetic retinopathy, fundus image, genetic algorithm, KSW entropy, image segmentation, hard exudates

CLC Number:

TP391

YANG Zhenyu,FU Yinghua,FU Dongxiang,WANG Yajing. HEs Segmentation of Fundus Images by Multi-algorithm Fusion[J].Electronic Science and Technology, 2020, 33(11): 24-30.

Figures/Tables 5

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Table 1

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References 30

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方法	Kaggle					DIARETDB1
	基于像素的评价标准		基于图像的评价标准			基于像素的评价标准		基于图像的评价标准
	SE/%	PPV/%	SE/%	SP/%	AC/%	SE/%	PPV/%	SE/%	SP/%	AC/%
文献[29]	—	—	—	—	—	84.6	94.4	97.3	90.0	93.7
文献[30]	—	—	—	—	—	92.4	87.1	—	81.3	87.7
文献[17]	93.2	79.3	97.1	80.0	95.0	—	—	—	—	—
本文算法	83.6	93.2	95.2	86.2	90.8	82.4	93.3	93.6	96.2	89.9

HEs Segmentation of Fundus Images by Multi-algorithm Fusion

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