Change Detection Based on Sparse Representation for Retina Fundus Image Pair
Fu Yinghua1*, Li Jiang1, Pan Dongyan2, Wang Guozheng1, Fu Dongxiang1
1(School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China) 2(Ophthalmology Department, Changhai Hospital ofthe Second Military Medical University, Shanghai 200433, China)
Abstract：Change detection with a pair of retinal fundus images was focused on comparing two images captured at different stages. Illumination variations between the image pair, along with the intensity similarity of anatomic structures and lesions make the pixel-by-pixel methods based on subtraction operation or ratio operation hard to obtain clear changing areas. In this paper, a new change detection method based on sparse representation classification (SRC) was proposed, aiming to reduce the illumination variations between the image pair. The SRC method first extracted the local neighborhood patches from the reference image to construct a local dictionary, then reconstructed the background of the current image by sparse representation on the extracted dictionary. Finally, change areas were obtained through background subtracting. The illumination variations between two images were corrected automatically by the representation coefficients, and SRC method based on patches can filter local contrast effectively to detect changing areas. A combination of SRC and some other change detection methods can improve the accuracy of the detection result. In the experiments of the passage, for a simulated image pair with small lesions, the AUC and mAP values were 0.985 and 0.864 respectively. For a clinical image pair with a big lesion, the AUC and mAP values of the combination of SRC and iterative robust homomorphic surface fitting (IRHSF) were 0.989 and 0.969 respectively. Experimental results showed that SRC was more robust than RPCA for the illumination variations and could detect the changing area more effectively than pixel-wised subtraction as it was involved with more neighborhood information.
傅迎华, 李江, 潘东艳, 王国政, 付东翔. 基于稀疏表示的视网膜图像对变化检测[J]. 中国生物医学工程学报, 2019, 38(5): 540-548.
Fu Yinghua, Li Jiang, Pan Dongyan, Wang Guozheng, Fu Dongxiang. Change Detection Based on Sparse Representation for Retina Fundus Image Pair. Chinese Journal of Biomedical Engineering, 2019, 38(5): 540-548.
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