Saliency-Based Segmentation of Optic Disc in Retinal Images
|
摘要
Accurate segmentation of Optic disc(OD)is significant for the automation of retinal analysis and retinal diseases screening. This paper proposes a novel optic disc segmentation method based on the saliency. It includes two stages: optic disc location and saliency-based segmentation. In the location stage, the OD is detected using a matched template and the density of the vessels.In the segmentation stage, we treat the OD as the salient object and formulate it as a saliency detection problem.To measure the saliency of a region, the boundary prior and the connectivity prior are exploited. Geodesic distance to the window boundary is computed to measure the cost the region spends to reach the window boundary.After a threshold and ellipse fitting, we obtain the OD.Experimental results on two public databases for OD segmentation show that the proposed method achieves thestate-of-the-art performance.
Accurate segmentation of Optic disc(OD)is significant for the automation of retinal analysis and retinal diseases screening. This paper proposes a novel optic disc segmentation method based on the saliency. It includes two stages: optic disc location and saliency-based segmentation. In the location stage, the OD is detected using a matched template and the density of the vessels.In the segmentation stage, we treat the OD as the salient object and formulate it as a saliency detection problem.To measure the saliency of a region, the boundary prior and the connectivity prior are exploited. Geodesic distance to the window boundary is computed to measure the cost the region spends to reach the window boundary.After a threshold and ellipse fitting, we obtain the OD.Experimental results on two public databases for OD segmentation show that the proposed method achieves thestate-of-the-art performance.
Accurate segmentation of Optic disc(OD)is significant for the automation of retinal analysis and retinal diseases screening. This paper proposes a novel optic disc segmentation method based on the saliency. It includes two stages: optic disc location and saliency-based segmentation. In the location stage, the OD is detected using a matched template and the density of the vessels.In the segmentation stage, we treat the OD as the salient object and formulate it as a saliency detection problem.To measure the saliency of a region, the boundary prior and the connectivity prior are exploited. Geodesic distance to the window boundary is computed to measure the cost the region spends to reach the window boundary.After a threshold and ellipse fitting, we obtain the OD.Experimental results on two public databases for OD segmentation show that the proposed method achieves thestate-of-the-art performance.
引文
[1]D.Pascolini and S.P.Mariotti,“Global estimates of visual impairment”,British Journal of Ophthalmology,doi:10.1136/bjophthalmol-2011-300539,2011.
[2]J.Cheng,F.Yin,D.W.K.Wong,et al.,“Sparse dissimilarityconstrained coding for glaucoma screening”,IEEETransactions on Biomedical Engineering,Vol.62,No.5,pp.1395-1403,2015.
[3]A.Singh,M.K.Dutta,M.ParthaSarathi,et al.,“Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image”,Computer Methods and Programs in Biomedicine,Vol.124,pp.108-120,2016.
[4]K.S.Chin,E.Trucco,L.Tan,et al.,“Automatic fovea location in retinal images using anatomical priors and vessel density”,Pattern Recognition Letters,Vol.34,No.10,pp.1152-1158,2013.
[5]Q.Liu,B.Zou,J.Chen,et al.,“A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images”,Computerized Medical Imaging and Graphics,Vol.55,pp.78-86,2017.
[6]M.Lalonde,M.Beaulieu,and L.Gagnon,“Fast and robust optic disc detection using pyramidal decomposition and hausdorf fbased template matching”,IEEE transactions on medical imaging,Vol.20,No.11,pp.1193-1200,2001.
[7]J.Lowell,A.Hunter,D.Steel,et al.,“Optic nerve head segmentation”,IEEE Transactions on medical Imaging,Vol.23,No.2,pp.256-264,2004.
[8]Q.Liu,B.Zou,J.Chen,et al.,“Retinal vessel segmentation from simple to difficult”,MICCAI workshop on ophthalmic medical image analysis,2016.
[9]H.Eviatar and R.L.Somorjai,“A fast,simple active contour algorithm for biomedical images”,Pattern Recognition Letters,Vol.17,No.9,pp.969-974,1996.
[10]S.Roychowdhury,D.D.Koozekanani and S.N.Kuchinka,et al.,“Optic disc boundary and vessel origin segmentation of fundus images”,IEEE Journal of Biomedical and Health Informatics,Vol.20,No.6,pp.1562-1574,2016.
[11]G.D.Joshi,J.Sivaswamy and S.Krishnadas,“Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment”,IEEE Transactions on Medical Imaging,Vol.30,No.6,pp.1192-1205,2011.
[12]J.Xu,O.Chutatape and E.Sung,et al.,“Optic disk feature extraction via modified deformable model technique for glaucoma analysis”,Pattern Recognition,Vol.40,No.7,pp.2063-2076,2007.
[13]A.Salazar-Gonzalez,D.Kaba,Y.Li,et al.,“Segmentation of the blood vessels and optic disk in retinal images”,IEEEJournal of Biomedical and Health Informatics,Vol.18,No.6,pp.1874-1886,2014.
[14]Y.Wei,F.Wen,W.Zhu,et al.,“Geodesic saliency using background priors”,European Conference on Computer Vision,Springer,pp.29-42,2012.
[15]B.Zou,Q.Liu,Z.Chen,et al.,“Saliency detection using boundary information”,Multimedia Systems,Vol.22,No.2,pp.245-253,2016.
[16]J.Zhang,D.Zhao,and W.Gao,“Group-based sparse representation for image restoration”,IEEE Transactions on Image Processing,Vol.23,No.8,pp.3336-3351,2014.
[17]H.Yu,E.S.Barriga,C.Agurto,et al.,“Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets”,IEEE Transactions on Information Technology in Biomedicine,Vol.16,No.4,pp.644-657,2012.
[18]R.Achanta,A.Shaji,K.Smith,et al.,“Slic superpixels compared to state-of-the-art superpixel methods”,IEEETransactions on Pattern Analysis and Machine Intelligence,Vol.34,No.11,pp.2274-2282,2012.
[19]E.J.Carmona,M.Rincn,J.Garca-Feijo,et al.,“Identification of the optic nerve head with genetic algorithms”,Artificial Intelligence in Medicine,Vol.43,No.3,pp.243-259,2008.
[20]E.Decencire,X.Zhang,G.Cazuguel,et al.,“Feedback on a publicly distributed database:The messidor database”,Image Analysis&Stereology,Vol.33,No.3,pp.231-234,2014.
[21]S.Morales,V.Naranjo,J.Angulo,et al.,“Automatic detection of optic disc based on PCA and mathematical morphology”,IEEE Transactions on Medical Imaging,Vol.32,No.4,pp.786-796,2013.
[22]T.Walter,J.C.Klein,P.Massin,et al.,“A contribution of image processing to the diagnosis of diabetic retinopathydetection of exudates in colour fundus images of the human retina”,IEEE Transactions on Medical Imaging,Vol.21,No.10,pp.1236-1243,2002.
[2]J.Cheng,F.Yin,D.W.K.Wong,et al.,“Sparse dissimilarityconstrained coding for glaucoma screening”,IEEETransactions on Biomedical Engineering,Vol.62,No.5,pp.1395-1403,2015.
[3]A.Singh,M.K.Dutta,M.ParthaSarathi,et al.,“Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image”,Computer Methods and Programs in Biomedicine,Vol.124,pp.108-120,2016.
[4]K.S.Chin,E.Trucco,L.Tan,et al.,“Automatic fovea location in retinal images using anatomical priors and vessel density”,Pattern Recognition Letters,Vol.34,No.10,pp.1152-1158,2013.
[5]Q.Liu,B.Zou,J.Chen,et al.,“A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images”,Computerized Medical Imaging and Graphics,Vol.55,pp.78-86,2017.
[6]M.Lalonde,M.Beaulieu,and L.Gagnon,“Fast and robust optic disc detection using pyramidal decomposition and hausdorf fbased template matching”,IEEE transactions on medical imaging,Vol.20,No.11,pp.1193-1200,2001.
[7]J.Lowell,A.Hunter,D.Steel,et al.,“Optic nerve head segmentation”,IEEE Transactions on medical Imaging,Vol.23,No.2,pp.256-264,2004.
[8]Q.Liu,B.Zou,J.Chen,et al.,“Retinal vessel segmentation from simple to difficult”,MICCAI workshop on ophthalmic medical image analysis,2016.
[9]H.Eviatar and R.L.Somorjai,“A fast,simple active contour algorithm for biomedical images”,Pattern Recognition Letters,Vol.17,No.9,pp.969-974,1996.
[10]S.Roychowdhury,D.D.Koozekanani and S.N.Kuchinka,et al.,“Optic disc boundary and vessel origin segmentation of fundus images”,IEEE Journal of Biomedical and Health Informatics,Vol.20,No.6,pp.1562-1574,2016.
[11]G.D.Joshi,J.Sivaswamy and S.Krishnadas,“Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment”,IEEE Transactions on Medical Imaging,Vol.30,No.6,pp.1192-1205,2011.
[12]J.Xu,O.Chutatape and E.Sung,et al.,“Optic disk feature extraction via modified deformable model technique for glaucoma analysis”,Pattern Recognition,Vol.40,No.7,pp.2063-2076,2007.
[13]A.Salazar-Gonzalez,D.Kaba,Y.Li,et al.,“Segmentation of the blood vessels and optic disk in retinal images”,IEEEJournal of Biomedical and Health Informatics,Vol.18,No.6,pp.1874-1886,2014.
[14]Y.Wei,F.Wen,W.Zhu,et al.,“Geodesic saliency using background priors”,European Conference on Computer Vision,Springer,pp.29-42,2012.
[15]B.Zou,Q.Liu,Z.Chen,et al.,“Saliency detection using boundary information”,Multimedia Systems,Vol.22,No.2,pp.245-253,2016.
[16]J.Zhang,D.Zhao,and W.Gao,“Group-based sparse representation for image restoration”,IEEE Transactions on Image Processing,Vol.23,No.8,pp.3336-3351,2014.
[17]H.Yu,E.S.Barriga,C.Agurto,et al.,“Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets”,IEEE Transactions on Information Technology in Biomedicine,Vol.16,No.4,pp.644-657,2012.
[18]R.Achanta,A.Shaji,K.Smith,et al.,“Slic superpixels compared to state-of-the-art superpixel methods”,IEEETransactions on Pattern Analysis and Machine Intelligence,Vol.34,No.11,pp.2274-2282,2012.
[19]E.J.Carmona,M.Rincn,J.Garca-Feijo,et al.,“Identification of the optic nerve head with genetic algorithms”,Artificial Intelligence in Medicine,Vol.43,No.3,pp.243-259,2008.
[20]E.Decencire,X.Zhang,G.Cazuguel,et al.,“Feedback on a publicly distributed database:The messidor database”,Image Analysis&Stereology,Vol.33,No.3,pp.231-234,2014.
[21]S.Morales,V.Naranjo,J.Angulo,et al.,“Automatic detection of optic disc based on PCA and mathematical morphology”,IEEE Transactions on Medical Imaging,Vol.32,No.4,pp.786-796,2013.
[22]T.Walter,J.C.Klein,P.Massin,et al.,“A contribution of image processing to the diagnosis of diabetic retinopathydetection of exudates in colour fundus images of the human retina”,IEEE Transactions on Medical Imaging,Vol.21,No.10,pp.1236-1243,2002.