Abstract

Detecting and quantifying the size of choroidal neovascularization (CNV) is important for the diagnosis and assessment of neovascular age-related macular degeneration. Depth-resolved imaging of the retinal and choroidal vasculature by optical coherence tomography angiography (OCTA) has enabled the visualization of CNV. However, due to the prevalence of artifacts, it is difficult to segment and quantify the CNV lesion area automatically. We have previously described a saliency algorithm for CNV detection that could identify a CNV lesion area with 83% accuracy. However, this method works under the assumption that the CNV region is the most salient area for visual attention in the whole image and consequently, errors occur when this requirement is not met (e.g. when the lesion occupies a large portion of the image). Moreover, saliency image processing methods cannot extract the edges of the salient object very accurately. In this paper, we propose a novel and automatic CNV segmentation method based on an unsupervised and parallel machine learning technique named density cell-like P systems (DEC P systems). DEC P systems integrate the idea of a modified clustering algorithm into cell-like P systems. This method improved the accuracy of detection to 87.2% on 22 subjects and obtained clear boundaries of the CNV lesions.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2017 (6)

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
[Crossref] [PubMed]

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
[Crossref] [PubMed]

J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography [Invited],” Biomed. Opt. Express 8(3), 1536–1548 (2017).
[Crossref] [PubMed]

H. Peng, P. Shi, J. Wang, A. Riscos-Nunez, and M. J. Perez-Jimenez, “Multiobjective fuzzy clustering approach based on tissue-like membrane systems,” Knowl. Base. Syst. 125, 74–82 (2017).
[Crossref]

A. Camino, Y. Jia, G. Liu, J. Wang, and D. Huang, “Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography,” Biomed. Opt. Express 8(6), 3053–3066 (2017).
[Crossref] [PubMed]

2016 (4)

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

J. Xue and X. Liu, “Communication P System with Oriented Chain Membrane Structures and Applications in Graph Clustering,” J. Comput. Theor. Nanosci. 13(7), 4198–4210 (2016).
[Crossref]

J. Xue, X. Liu, and P. Chen, “Rhombic Grid Based Clustering Algorithm with Spiking Neural P Systems,” J. Comput. Theor. Nanosci. 13(6), 3895–3901 (2016).
[Crossref]

M. Zhang, T. S. Hwang, J. P. Campbell, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Projection-resolved optical coherence tomographic angiography,” Biomed. Opt. Express 7(3), 816–828 (2016).
[Crossref] [PubMed]

2015 (5)

2014 (3)

J. Xue and X. Liu, “Lattice based communication P systems with applications in cluster analysis,” Soft Comput. 18(7), 1425–1440 (2014).
[Crossref]

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref] [PubMed]

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
[Crossref] [PubMed]

2013 (2)

D. Díaz-Pernil, A. Berciano, F. Peña-Cantillana, and M. A. Gutiérrez-Naranjo, “Segmenting images with gradient-based edge detection using Membrane Computing,” Pattern Recognit. Lett. 34(8), 846–855 (2013).
[Crossref]

G. Zhang, J. Cheng, M. Gheorghe, and Q. Meng, “A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems,” Appl. Soft Comput. 13(3), 1528–1542 (2013).
[Crossref]

2012 (3)

2011 (3)

M. Hemalatha and N. N. Saranya, “A Recent Survey on Knowledge Discovery in Spatial Data Mining,” Int. J. Comput. Sci. 8, 473–479 (2011).

L. Huang, I. H. Suh, and A. Abraham, “Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants,” Inf. Sci. 181(11), 2370–2391 (2011).
[Crossref]

H. A. Christinal, D. Díaz-Pernil, and P. Real, “Region-based segmentation of 2D and 3D images with tissue-like P systems,” Pattern Recognit. Lett. 32(16), 2206–2212 (2011).
[Crossref]

2010 (1)

2008 (1)

L. A. Yannuzzi, K. B. Freund, and B. S. Takahashi, “Review of retinal angiomatous proliferation or type 3 neovascularization,” Retina 28(3), 375–384 (2008).
[Crossref] [PubMed]

2007 (2)

D. Birant and A. Kut, “ST-DBSCAN: An algorithm for clustering spatial–temporal data,” Data Knowl. Eng. 60(1), 208–221 (2007).
[Crossref]

M. A. Gutiérrez-Naranjo, M. J. Pérez-Jiménez, and F. J. Romero-Campero, “A uniform solution to SAT using membrane creation,” Theor. Comput. Sci. 371(1-2), 54–61 (2007).
[Crossref]

2006 (1)

G. Păun and M. J. Pérez-Jiménez, “Membrane computing: brief introduction, recent results and applications,” Biosystems 85(1), 11–22 (2006).
[Crossref] [PubMed]

2003 (1)

A. W. Liew and H. Yan, “An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation,” IEEE Trans. Med. Imaging 22(9), 1063–1075 (2003).
[Crossref] [PubMed]

2000 (1)

G. Păun, “Computing with Membranes,” J. Comput. Syst. Sci. 61(1), 108–143 (2000).
[Crossref]

1999 (1)

A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Comput. Surv. 31(3), 264–323 (1999).
[Crossref]

Abraham, A.

L. Huang, I. H. Suh, and A. Abraham, “Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants,” Inf. Sci. 181(11), 2370–2391 (2011).
[Crossref]

An, L.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
[Crossref] [PubMed]

Bailey, S. T.

M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
[Crossref] [PubMed]

J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography [Invited],” Biomed. Opt. Express 8(3), 1536–1548 (2017).
[Crossref] [PubMed]

M. Zhang, T. S. Hwang, J. P. Campbell, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Projection-resolved optical coherence tomographic angiography,” Biomed. Opt. Express 7(3), 816–828 (2016).
[Crossref] [PubMed]

L. Liu, S. S. Gao, S. T. Bailey, D. Huang, D. Li, and Y. Jia, “Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography,” Biomed. Opt. Express 6(9), 3564–3576 (2015).
[Crossref] [PubMed]

M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
[Crossref] [PubMed]

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
[Crossref] [PubMed]

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref] [PubMed]

Baumann, B.

Berciano, A.

D. Díaz-Pernil, A. Berciano, F. Peña-Cantillana, and M. A. Gutiérrez-Naranjo, “Segmenting images with gradient-based edge detection using Membrane Computing,” Pattern Recognit. Lett. 34(8), 846–855 (2013).
[Crossref]

Birant, D.

D. Birant and A. Kut, “ST-DBSCAN: An algorithm for clustering spatial–temporal data,” Data Knowl. Eng. 60(1), 208–221 (2007).
[Crossref]

Bock, R.

Camino, A.

Campbell, J. P.

Chen, C. L.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Chen, P.

J. Xue, X. Liu, and P. Chen, “Rhombic Grid Based Clustering Algorithm with Spiking Neural P Systems,” J. Comput. Theor. Nanosci. 13(6), 3895–3901 (2016).
[Crossref]

Cheng, C. Y.

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
[Crossref] [PubMed]

Cheng, J.

G. Zhang, J. Cheng, M. Gheorghe, and Q. Meng, “A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems,” Appl. Soft Comput. 13(3), 1528–1542 (2013).
[Crossref]

Cheung, C. M.

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
[Crossref] [PubMed]

Chiu, S. J.

Choi, W.

Christinal, H. A.

H. A. Christinal, D. Díaz-Pernil, and P. Real, “Region-based segmentation of 2D and 3D images with tissue-like P systems,” Pattern Recognit. Lett. 32(16), 2206–2212 (2011).
[Crossref]

Chu, Z.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Díaz-Pernil, D.

D. Díaz-Pernil, A. Berciano, F. Peña-Cantillana, and M. A. Gutiérrez-Naranjo, “Segmenting images with gradient-based edge detection using Membrane Computing,” Pattern Recognit. Lett. 34(8), 846–855 (2013).
[Crossref]

H. A. Christinal, D. Díaz-Pernil, and P. Real, “Region-based segmentation of 2D and 3D images with tissue-like P systems,” Pattern Recognit. Lett. 32(16), 2206–2212 (2011).
[Crossref]

Durbin, M. K.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
[Crossref] [PubMed]

Farsiu, S.

Feuer, W.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Flaxel, C.

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M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
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M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
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M. Zhang, T. S. Hwang, J. P. Campbell, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Projection-resolved optical coherence tomographic angiography,” Biomed. Opt. Express 7(3), 816–828 (2016).
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S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
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M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
[Crossref] [PubMed]

L. Liu, S. S. Gao, S. T. Bailey, D. Huang, D. Li, and Y. Jia, “Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography,” Biomed. Opt. Express 6(9), 3564–3576 (2015).
[Crossref] [PubMed]

S. S. Gao, G. Liu, D. Huang, and Y. Jia, “Optimization of the split-spectrum amplitude-decorrelation angiography algorithm on a spectral optical coherence tomography system,” Opt. Lett. 40(10), 2305–2308 (2015).
[Crossref] [PubMed]

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref] [PubMed]

Y. Jia, J. C. Morrison, J. Tokayer, O. Tan, L. Lombardi, B. Baumann, C. D. Lu, W. Choi, J. G. Fujimoto, and D. Huang, “Quantitative OCT angiography of optic nerve head blood flow,” Biomed. Opt. Express 3(12), 3127–3137 (2012).
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M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
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J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography [Invited],” Biomed. Opt. Express 8(3), 1536–1548 (2017).
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A. Camino, Y. Jia, G. Liu, J. Wang, and D. Huang, “Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography,” Biomed. Opt. Express 8(6), 3053–3066 (2017).
[Crossref] [PubMed]

M. Zhang, T. S. Hwang, J. P. Campbell, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Projection-resolved optical coherence tomographic angiography,” Biomed. Opt. Express 7(3), 816–828 (2016).
[Crossref] [PubMed]

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
[Crossref] [PubMed]

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
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M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
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S. S. Gao, G. Liu, D. Huang, and Y. Jia, “Optimization of the split-spectrum amplitude-decorrelation angiography algorithm on a spectral optical coherence tomography system,” Opt. Lett. 40(10), 2305–2308 (2015).
[Crossref] [PubMed]

L. Liu, S. S. Gao, S. T. Bailey, D. Huang, D. Li, and Y. Jia, “Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography,” Biomed. Opt. Express 6(9), 3564–3576 (2015).
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Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref] [PubMed]

Y. Jia, J. C. Morrison, J. Tokayer, O. Tan, L. Lombardi, B. Baumann, C. D. Lu, W. Choi, J. G. Fujimoto, and D. Huang, “Quantitative OCT angiography of optic nerve head blood flow,” Biomed. Opt. Express 3(12), 3127–3137 (2012).
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Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
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M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
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Li, X.

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
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M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
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McClintic, S. M.

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
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S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
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Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
[Crossref] [PubMed]

Perez-Jimenez, M. J.

H. Peng, P. Shi, J. Wang, A. Riscos-Nunez, and M. J. Perez-Jimenez, “Multiobjective fuzzy clustering approach based on tissue-like membrane systems,” Knowl. Base. Syst. 125, 74–82 (2017).
[Crossref]

Pérez-Jiménez, M. J.

M. A. Gutiérrez-Naranjo, M. J. Pérez-Jiménez, and F. J. Romero-Campero, “A uniform solution to SAT using membrane creation,” Theor. Comput. Sci. 371(1-2), 54–61 (2007).
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G. Păun and M. J. Pérez-Jiménez, “Membrane computing: brief introduction, recent results and applications,” Biosystems 85(1), 11–22 (2006).
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Potsaid, B.

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref] [PubMed]

M. F. Kraus, B. Potsaid, M. A. Mayer, R. Bock, B. Baumann, J. J. Liu, J. Hornegger, and J. G. Fujimoto, “Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns,” Biomed. Opt. Express 3(6), 1182–1199 (2012).
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Rafael de Oliveira Dias, J.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
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H. A. Christinal, D. Díaz-Pernil, and P. Real, “Region-based segmentation of 2D and 3D images with tissue-like P systems,” Pattern Recognit. Lett. 32(16), 2206–2212 (2011).
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Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
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Riscos-Nunez, A.

H. Peng, P. Shi, J. Wang, A. Riscos-Nunez, and M. J. Perez-Jimenez, “Multiobjective fuzzy clustering approach based on tissue-like membrane systems,” Knowl. Base. Syst. 125, 74–82 (2017).
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Roisman, L.

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
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Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
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Romero-Campero, F. J.

M. A. Gutiérrez-Naranjo, M. J. Pérez-Jiménez, and F. J. Romero-Campero, “A uniform solution to SAT using membrane creation,” Theor. Comput. Sci. 371(1-2), 54–61 (2007).
[Crossref]

Rosenfeld, P. J.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
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Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
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M. Hemalatha and N. N. Saranya, “A Recent Survey on Knowledge Discovery in Spatial Data Mining,” Int. J. Comput. Sci. 8, 473–479 (2011).

Schaal, K. B.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Shi, P.

H. Peng, P. Shi, J. Wang, A. Riscos-Nunez, and M. J. Perez-Jimenez, “Multiobjective fuzzy clustering approach based on tissue-like membrane systems,” Knowl. Base. Syst. 125, 74–82 (2017).
[Crossref]

Stetson, P. F.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
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Su, X.

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
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L. Huang, I. H. Suh, and A. Abraham, “Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants,” Inf. Sci. 181(11), 2370–2391 (2011).
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Takahashi, B. S.

L. A. Yannuzzi, K. B. Freund, and B. S. Takahashi, “Review of retinal angiomatous proliferation or type 3 neovascularization,” Retina 28(3), 375–384 (2008).
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S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
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Tal, A.

S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012).
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Tan, O.

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref] [PubMed]

Y. Jia, J. C. Morrison, J. Tokayer, O. Tan, L. Lombardi, B. Baumann, C. D. Lu, W. Choi, J. G. Fujimoto, and D. Huang, “Quantitative OCT angiography of optic nerve head blood flow,” Biomed. Opt. Express 3(12), 3127–3137 (2012).
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Tokayer, J.

Toth, C. A.

Wang, J.

Wang, R. K.

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
[Crossref] [PubMed]

A. Zhang, Q. Zhang, and R. K. Wang, “Minimizing projection artifacts for accurate presentation of choroidal neovascularization in OCT micro-angiography,” Biomed. Opt. Express 6(10), 4130–4143 (2015).
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Wang, R. K. K.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

Wilson, D. J.

M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
[Crossref] [PubMed]

J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography [Invited],” Biomed. Opt. Express 8(3), 1536–1548 (2017).
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M. Zhang, T. S. Hwang, J. P. Campbell, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Projection-resolved optical coherence tomographic angiography,” Biomed. Opt. Express 7(3), 816–828 (2016).
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M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
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Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
[Crossref] [PubMed]

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
[Crossref] [PubMed]

Wong, T. Y.

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
[Crossref] [PubMed]

Wong, W. L.

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
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Xue, J.

J. Xue, X. Liu, and P. Chen, “Rhombic Grid Based Clustering Algorithm with Spiking Neural P Systems,” J. Comput. Theor. Nanosci. 13(6), 3895–3901 (2016).
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J. Xue and X. Liu, “Communication P System with Oriented Chain Membrane Structures and Applications in Graph Clustering,” J. Comput. Theor. Nanosci. 13(7), 4198–4210 (2016).
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J. Xue and X. Liu, “Lattice based communication P systems with applications in cluster analysis,” Soft Comput. 18(7), 1425–1440 (2014).
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Yan, H.

A. W. Liew and H. Yan, “An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation,” IEEE Trans. Med. Imaging 22(9), 1063–1075 (2003).
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Yannuzzi, L. A.

L. A. Yannuzzi, K. B. Freund, and B. S. Takahashi, “Review of retinal angiomatous proliferation or type 3 neovascularization,” Retina 28(3), 375–384 (2008).
[Crossref] [PubMed]

Yehoshua, Z.

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
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Zelnik-Manor, L.

S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012).
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Zhang, A.

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
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A. Zhang, Q. Zhang, and R. K. Wang, “Minimizing projection artifacts for accurate presentation of choroidal neovascularization in OCT micro-angiography,” Biomed. Opt. Express 6(10), 4130–4143 (2015).
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Zhang, G.

G. Zhang, J. Cheng, M. Gheorghe, and Q. Meng, “A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems,” Appl. Soft Comput. 13(3), 1528–1542 (2013).
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Zhang, Q.

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
[Crossref] [PubMed]

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

A. Zhang, Q. Zhang, and R. K. Wang, “Minimizing projection artifacts for accurate presentation of choroidal neovascularization in OCT micro-angiography,” Biomed. Opt. Express 6(10), 4130–4143 (2015).
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Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
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Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
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ACM Comput. Surv. (1)

A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Comput. Surv. 31(3), 264–323 (1999).
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Appl. Soft Comput. (1)

G. Zhang, J. Cheng, M. Gheorghe, and Q. Meng, “A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems,” Appl. Soft Comput. 13(3), 1528–1542 (2013).
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Biomed. Opt. Express (8)

M. F. Kraus, B. Potsaid, M. A. Mayer, R. Bock, B. Baumann, J. J. Liu, J. Hornegger, and J. G. Fujimoto, “Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns,” Biomed. Opt. Express 3(6), 1182–1199 (2012).
[Crossref] [PubMed]

M. Zhang, J. Wang, A. D. Pechauer, T. S. Hwang, S. S. Gao, L. Liu, L. Liu, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Advanced image processing for optical coherence tomographic angiography of macular diseases,” Biomed. Opt. Express 6(12), 4661–4675 (2015).
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A. Camino, Y. Jia, G. Liu, J. Wang, and D. Huang, “Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography,” Biomed. Opt. Express 8(6), 3053–3066 (2017).
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L. Liu, S. S. Gao, S. T. Bailey, D. Huang, D. Li, and Y. Jia, “Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography,” Biomed. Opt. Express 6(9), 3564–3576 (2015).
[Crossref] [PubMed]

M. Zhang, T. S. Hwang, J. P. Campbell, S. T. Bailey, D. J. Wilson, D. Huang, and Y. Jia, “Projection-resolved optical coherence tomographic angiography,” Biomed. Opt. Express 7(3), 816–828 (2016).
[Crossref] [PubMed]

A. Zhang, Q. Zhang, and R. K. Wang, “Minimizing projection artifacts for accurate presentation of choroidal neovascularization in OCT micro-angiography,” Biomed. Opt. Express 6(10), 4130–4143 (2015).
[Crossref] [PubMed]

J. Wang, M. Zhang, T. S. Hwang, S. T. Bailey, D. Huang, D. J. Wilson, and Y. Jia, “Reflectance-based projection-resolved optical coherence tomography angiography [Invited],” Biomed. Opt. Express 8(3), 1536–1548 (2017).
[Crossref] [PubMed]

Y. Jia, J. C. Morrison, J. Tokayer, O. Tan, L. Lombardi, B. Baumann, C. D. Lu, W. Choi, J. G. Fujimoto, and D. Huang, “Quantitative OCT angiography of optic nerve head blood flow,” Biomed. Opt. Express 3(12), 3127–3137 (2012).
[Crossref] [PubMed]

Biosystems (1)

G. Păun and M. J. Pérez-Jiménez, “Membrane computing: brief introduction, recent results and applications,” Biosystems 85(1), 11–22 (2006).
[Crossref] [PubMed]

Br. J. Ophthalmol. (1)

M. Malihi, Y. Jia, S. S. Gao, C. Flaxel, A. K. Lauer, T. Hwang, D. J. Wilson, D. Huang, and S. T. Bailey, “Optical coherence tomographic angiography of choroidal neovascularization ill-defined with fluorescein angiography,” Br. J. Ophthalmol. 101(1), 45–50 (2017).
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IEEE Trans. Med. Imaging (1)

A. W. Liew and H. Yan, “An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation,” IEEE Trans. Med. Imaging 22(9), 1063–1075 (2003).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012).
[Crossref] [PubMed]

Inf. Sci. (1)

L. Huang, I. H. Suh, and A. Abraham, “Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants,” Inf. Sci. 181(11), 2370–2391 (2011).
[Crossref]

Int. J. Comput. Sci. (1)

M. Hemalatha and N. N. Saranya, “A Recent Survey on Knowledge Discovery in Spatial Data Mining,” Int. J. Comput. Sci. 8, 473–479 (2011).

Invest. Ophthalmol. Vis. Sci. (2)

Q. Zhang, C. L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira Dias, Z. Yehoshua, K. B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P. F. Stetson, M. K. Durbin, P. J. Rosenfeld, and R. K. K. Wang, “Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms,” Invest. Ophthalmol. Vis. Sci. 58(3), 1506–1513 (2017).
[Crossref] [PubMed]

S. S. Gao, Y. Jia, L. Liu, M. Zhang, H. L. Takusagawa, J. C. Morrison, and D. Huang, “Compensation for Reflectance Variation in Vessel Density Quantification by Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(10), 4485–4492 (2016).
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J. Xue and X. Liu, “Communication P System with Oriented Chain Membrane Structures and Applications in Graph Clustering,” J. Comput. Theor. Nanosci. 13(7), 4198–4210 (2016).
[Crossref]

J. Xue, X. Liu, and P. Chen, “Rhombic Grid Based Clustering Algorithm with Spiking Neural P Systems,” J. Comput. Theor. Nanosci. 13(6), 3895–3901 (2016).
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Knowl. Base. Syst. (1)

H. Peng, P. Shi, J. Wang, A. Riscos-Nunez, and M. J. Perez-Jimenez, “Multiobjective fuzzy clustering approach based on tissue-like membrane systems,” Knowl. Base. Syst. 125, 74–82 (2017).
[Crossref]

Lancet Glob. Health (1)

W. L. Wong, X. Su, X. Li, C. M. Cheung, R. Klein, C. Y. Cheng, and T. Y. Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis,” Lancet Glob. Health 2(2), e106–e116 (2014).
[Crossref] [PubMed]

Ophthalmol Retina (1)

Q. Zhang, A. Zhang, C. S. Lee, A. Y. Lee, K. A. Rezaei, L. Roisman, A. Miller, F. Zheng, G. Gregori, M. K. Durbin, L. An, P. F. Stetson, P. J. Rosenfeld, and R. K. Wang, “Projection artifact removal improves visualization and quantitation of macular neovascularization imaged by optical coherence tomography angiography,” Ophthalmol Retina 1(2), 124–136 (2017).
[Crossref] [PubMed]

Ophthalmology (1)

Y. Jia, S. T. Bailey, D. J. Wilson, O. Tan, M. L. Klein, C. J. Flaxel, B. Potsaid, J. J. Liu, C. D. Lu, M. F. Kraus, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration,” Ophthalmology 121(7), 1435–1444 (2014).
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Opt. Express (1)

Opt. Lett. (1)

Pattern Recognit. Lett. (2)

H. A. Christinal, D. Díaz-Pernil, and P. Real, “Region-based segmentation of 2D and 3D images with tissue-like P systems,” Pattern Recognit. Lett. 32(16), 2206–2212 (2011).
[Crossref]

D. Díaz-Pernil, A. Berciano, F. Peña-Cantillana, and M. A. Gutiérrez-Naranjo, “Segmenting images with gradient-based edge detection using Membrane Computing,” Pattern Recognit. Lett. 34(8), 846–855 (2013).
[Crossref]

Proc. Natl. Acad. Sci. U.S.A. (1)

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015).
[Crossref] [PubMed]

Retina (1)

L. A. Yannuzzi, K. B. Freund, and B. S. Takahashi, “Review of retinal angiomatous proliferation or type 3 neovascularization,” Retina 28(3), 375–384 (2008).
[Crossref] [PubMed]

Soft Comput. (1)

J. Xue and X. Liu, “Lattice based communication P systems with applications in cluster analysis,” Soft Comput. 18(7), 1425–1440 (2014).
[Crossref]

Theor. Comput. Sci. (1)

M. A. Gutiérrez-Naranjo, M. J. Pérez-Jiménez, and F. J. Romero-Campero, “A uniform solution to SAT using membrane creation,” Theor. Comput. Sci. 371(1-2), 54–61 (2007).
[Crossref]

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Figures (6)

Fig. 1
Fig. 1 Framework of CNV identification based on a DEC P system. A projection-resolved en face outer retinal angiogram was obtained in step 1 [31]. A Gaussian filter was used to smooth the CNV structure and the position of non-zero pixels are extracted in step 2. Step 3 sorts the distances of non-zero pixels to every other non-zero pixel and fits them into a Gaussian function to obtain parameters ε and MinPts, as explained in section 2.3. Step 4 describes the execution of DEC P system for CNV detection, explained in detail in section 2.3. Step 5 shows the clustering result of the DEC P system method and morphological operations were used to generate the final CNV lesion area.
Fig. 2
Fig. 2 X p and X q are directly density-reachable to each other; X p and X o are density-connected to each other by X q ; X p is density-reachable from X t through X p 1 , X p 2 and X p 3 ; Circles represent a neighborhood of radius ε for each pixel of interest.
Fig. 3
Fig. 3 Performance of the DEC P systems and the saliency method compared to manual delineation for three examples. Each row case 1, case 2 and case 3 represent an outer retinal angiogram with CNV. Green and blue arrows point at positions where DEC P systems and saliency differ from manual segmentation respectively. The regions of disagreement are noted in the manual segmentation images with dashed ellipses. Note that the ellipses representing disagreement with the DEC P systems method (green color) are smaller in size than the ones representing disagreement with the saliency method (blue color). Jaccard similarity was larger for the DEC P systems segmentation.
Fig. 4
Fig. 4 Performance evaluation of the DEC P systems method and the saliency method for extracting CNV vessels in Fig. 3 (case 1). Blue arrows point at positions that saliency method cuts the full length of vessels at boundaries, whereas, DEC P system recognize the boundries well.
Fig. 5
Fig. 5 Performance of the DEC P systems with different parameters on the case in Ref. 3 (case 1). Green arrows point at positions where DEC P systems with suitable parameters ε and MinPts differ from manual segmentation. Purple arrows indicate positions where DEC P systems with paramerters of εk(5) disagree with the manual segmentation. The regions of disagreement are noted in the manual segmentation images with dashed ellipses. Note that DEC P systems with parameters of εk( 5 )contains more noise than the one with the suitable parameter.
Fig. 6
Fig. 6 Performance of the DEC P systems method and the saliency method compared to manual delineation in the scan that noise pixels are bright and found within the vicinity of vessels. Green arrows point at positions where DEC P systems differ from manual segmentation. The regions of disagreement are noted in the manual segmentation images with dashed ellipses.

Tables (2)

Tables Icon

Table 1 Values of ε, MinPts and range of fcorresponding to each cell’s evolution rules.

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Table 2 Mean and standard deviation of the Jaccard coefficient and error rates on the CNV areas of the 22 cases.

Equations (11)

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=(X,m,H,μ,S,ε,MinPts,Ev,Co, i 0 )
dis( X p , X q )= ( x p x q ) 2 + ( y p y q ) 2
f= a 1 e ((k b 1 )/ c 1 ) 2 + a 2 e ((k b 2 )/ c 2 ) 2 + a 3 e ((k b 3 )/ c 3 ) 2 + a 4 e ((k b 4 )/ c 4 ) 2 + a 5 e ((k b 5 )/ c 5 ) 2 + a 6 e ((k b 6 )/ c 6 ) 2 + a 7 e ((k b 7 )/ c 7 ) 2 + a 8 e ((k b 8 )/ c 8 ) 2
[ [ [ [ [ [ X p [ X p ' ] p ] 6 ] 5 ] 4 ] 3 ] 2 ] 1
[ [ [ [ [ [ [ X o X q ' ] q [ X o X p ' ] p [ X o X p X q ] p ] 6 ] 5 ] 4 ] 3 ] 2 ] 1
[ [ [ [ [ [ X q [] p [ X q t] p ] 6 ] 5 ] 4 ] 3 ] 2 ] 1
[ [ [ [ [ [ [ X i ] p [] p C ip ] 6 ] 5 ] 4 ] 3 ] 2 ] 1
[ [ [ [ [ [] 6 ] 5 ] 4 ] 3 C ip ] 2 ] 1 [ [ [ [ [ [] 6 ] 5 ] 4 ] 3 ] 2 C ip ] 1
J( C P , C M )= | C P C M | | C P C M |
FPR=FP/( FP + TN )
FNR=FN/( FN + TP )

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