Abstract

This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.

© 2012 OSA

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2012

2011

S. Lu, “Accurate and efficient optic disc detection and segmentation by a circular transformation,” IEEE Trans. Med. Imaging30(12), 2126–2133 (2011).
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M. Pircher, J. S. Kroisamer, F. Felberer, H. Sattmann, E. Götzinger, and C. K. Hitzenberger, “Temporal changes of human cone photoreceptors observed in vivo with SLO/OCT,” Biomed. Opt. Express2(1), 100–112 (2011).
[CrossRef] [PubMed]

A. H. Karimi, A. Wong, and K. Bizheva, “Automated detection and cell density assessment of keratocytes in the human corneal stroma from ultrahigh resolution optical coherence tomograms,” Biomed. Opt. Express2(10), 2905–2916 (2011).
[CrossRef] [PubMed]

A. D. Mora, P. M. Vieira, A. Manivannan, and J. M. Fonseca, “Automated drusen detection in retinal images using analytical modelling algorithms,” Biomed. Eng. Online10(1), 59 (2011).
[CrossRef] [PubMed]

R. F. Cooper, A. M. Dubis, A. Pavaskar, J. Rha, A. Dubra, and J. Carroll, “Spatial and temporal variation of rod photoreceptor reflectance in the human retina,” Biomed. Opt. Express2(9), 2577–2589 (2011).
[CrossRef] [PubMed]

J. D. Ding, L. V. Johnson, R. Herrmann, S. Farsiu, S. G. Smith, M. Groelle, B. E. Mace, P. Sullivan, J. A. Jamison, U. Kelly, O. Harrabi, S. S. Bollini, J. Dilley, D. Kobayashi, B. Kuang, W. Li, J. Pons, J. C. Lin, and C. B. Rickman, “Anti-amyloid therapy protects against retinal pigmented epithelium damage and vision loss in a model of age-related macular degeneration,” Proc. Natl. Acad. Sci. U.S.A.108(28), E279–E287 (2011).
[CrossRef] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging30(2), 484–496 (2011).
[CrossRef] [PubMed]

K. A. Vermeer, J. van der Schoot, H. G. Lemij, and J. F. de Boer, “Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images,” Biomed. Opt. Express2(6), 1743–1756 (2011).
[CrossRef] [PubMed]

Y. Y. Liu, M. Chen, H. Ishikawa, G. Wollstein, J. S. Schuman, and J. M. Rehg, “Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding,” Med. Image Anal.15(5), 748–759 (2011).
[CrossRef] [PubMed]

Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa,” Biomed. Opt. Express2(9), 2493–2503 (2011).
[CrossRef] [PubMed]

2010

2008

C. Ahlers, C. Simader, W. Geitzenauer, G. Stock, P. Stetson, S. Dastmalchi, and U. Schmidt-Erfurth, “Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography,” Br. J. Ophthalmol.92(2), 197–203 (2008).
[CrossRef] [PubMed]

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE6844, 68440D, 68440D-12 (2008).
[CrossRef]

2007

2005

A. Ruggeri, E. Grisan, and J. Jaroszewski, “A new system for the automatic estimation of endothelial cell density in donor corneas,” Br. J. Ophthalmol.89(3), 306–311 (2005).
[CrossRef] [PubMed]

R. T. Smith, J. K. Chan, T. Nagasaki, U. F. Ahmad, I. Barbazetto, J. Sparrow, M. Figueroa, and J. Merriam, “Automated detection of macular drusen using geometric background leveling and threshold selection,” Arch. Ophthalmol.123(2), 200–206 (2005).
[CrossRef] [PubMed]

D. C. Fernández, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imaging24(8), 929–945 (2005).
[CrossRef] [PubMed]

2004

S. Timp and N. Karssemeijer, “A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography,” Med. Phys.31(5), 958–971 (2004).
[CrossRef] [PubMed]

N. M. Bressler, “Age-related macular degeneration is the leading cause of blindness,” JAMA291(15), 1900–1901 (2004).
[CrossRef] [PubMed]

2002

C. A. Glasbey and M. J. Young, “Maximum a posteriori estimation of image boundaries by dynamic programming,” J. R. Stat. Soc. Ser. C Appl. Stat.51(2), 209–221 (2002).
[CrossRef]

A. Roorda, F. Romero-Borja, W. Donnelly Iii, H. Queener, T. Hebert, and M. Campbell, “Adaptive optics scanning laser ophthalmoscopy,” Opt. Express10(9), 405–412 (2002).
[PubMed]

2001

1999

T. Otani, S. Kishi, and Y. Maruyama, “Patterns of diabetic macular edema with optical coherence tomography,” Am. J. Ophthalmol.127(6), 688–693 (1999).
[CrossRef] [PubMed]

S. V. Patel, J. W. McLaren, J. J. Camp, L. R. Nelson, and W. M. Bourne, “Automated quantification of keratocyte density by using confocal microscopy in vivo,” Invest. Ophthalmol. Vis. Sci.40(2), 320–326 (1999).
[PubMed]

F. J. Sanchez-Marin, “Automatic segmentation of contours of corneal cells,” Comput. Biol. Med.29(4), 243–258 (1999).
[CrossRef] [PubMed]

1998

M. R. Hee, C. A. Puliafito, J. S. Duker, E. Reichel, J. G. Coker, J. R. Wilkins, J. S. Schuman, E. A. Swanson, and J. G. Fujimoto, “Topography of diabetic macular edema with optical coherence tomography,” Ophthalmology105(2), 360–370 (1998).
[CrossRef] [PubMed]

1997

1991

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).
[CrossRef] [PubMed]

1959

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik1(1), 269–271 (1959).
[CrossRef]

Abramoff,, M. D.

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abramoff,, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging29(6), 1321–1330 (2010).
[CrossRef] [PubMed]

Ahlers, C.

C. Ahlers, C. Simader, W. Geitzenauer, G. Stock, P. Stetson, S. Dastmalchi, and U. Schmidt-Erfurth, “Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography,” Br. J. Ophthalmol.92(2), 197–203 (2008).
[CrossRef] [PubMed]

Ahmad, U. F.

R. T. Smith, J. K. Chan, T. Nagasaki, U. F. Ahmad, I. Barbazetto, J. Sparrow, M. Figueroa, and J. Merriam, “Automated detection of macular drusen using geometric background leveling and threshold selection,” Arch. Ophthalmol.123(2), 200–206 (2005).
[CrossRef] [PubMed]

Ayala, G.

M. E. Díaz, G. Ayala, R. Sebastian, and L. Martínez-Costa, “Granulometric analysis of corneal endothelium specular images by using a germ-grain model,” Comput. Biol. Med.37(3), 364–375 (2007).
[CrossRef] [PubMed]

Barbazetto, I.

R. T. Smith, J. K. Chan, T. Nagasaki, U. F. Ahmad, I. Barbazetto, J. Sparrow, M. Figueroa, and J. Merriam, “Automated detection of macular drusen using geometric background leveling and threshold selection,” Arch. Ophthalmol.123(2), 200–206 (2005).
[CrossRef] [PubMed]

Bizheva, K.

Bollini, S. S.

J. D. Ding, L. V. Johnson, R. Herrmann, S. Farsiu, S. G. Smith, M. Groelle, B. E. Mace, P. Sullivan, J. A. Jamison, U. Kelly, O. Harrabi, S. S. Bollini, J. Dilley, D. Kobayashi, B. Kuang, W. Li, J. Pons, J. C. Lin, and C. B. Rickman, “Anti-amyloid therapy protects against retinal pigmented epithelium damage and vision loss in a model of age-related macular degeneration,” Proc. Natl. Acad. Sci. U.S.A.108(28), E279–E287 (2011).
[CrossRef] [PubMed]

Bourantas, G.

S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas, and G. C. Nikiforidis, “Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography,” Med. Phys.39(1), 503–513 (2012).
[CrossRef] [PubMed]

Bourne, W. M.

S. V. Patel, J. W. McLaren, J. J. Camp, L. R. Nelson, and W. M. Bourne, “Automated quantification of keratocyte density by using confocal microscopy in vivo,” Invest. Ophthalmol. Vis. Sci.40(2), 320–326 (1999).
[PubMed]

Bressler, N. M.

N. M. Bressler, “Age-related macular degeneration is the leading cause of blindness,” JAMA291(15), 1900–1901 (2004).
[CrossRef] [PubMed]

Camp, J. J.

S. V. Patel, J. W. McLaren, J. J. Camp, L. R. Nelson, and W. M. Bourne, “Automated quantification of keratocyte density by using confocal microscopy in vivo,” Invest. Ophthalmol. Vis. Sci.40(2), 320–326 (1999).
[PubMed]

Campbell, M.

Carroll, J.

Chan, J. K.

R. T. Smith, J. K. Chan, T. Nagasaki, U. F. Ahmad, I. Barbazetto, J. Sparrow, M. Figueroa, and J. Merriam, “Automated detection of macular drusen using geometric background leveling and threshold selection,” Arch. Ophthalmol.123(2), 200–206 (2005).
[CrossRef] [PubMed]

Chan, K.

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991).
[CrossRef] [PubMed]

Chen, L.

Chen, M.

Y. Y. Liu, M. Chen, H. Ishikawa, G. Wollstein, J. S. Schuman, and J. M. Rehg, “Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding,” Med. Image Anal.15(5), 748–759 (2011).
[CrossRef] [PubMed]

Chiu, S. J.

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci.53(1), 53–61 (2012).
[CrossRef] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express2(6), 1524–1538 (2011).
[CrossRef] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18(18), 19413–19428 (2010).
[CrossRef] [PubMed]

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE6844, 68440D, 68440D-12 (2008).
[CrossRef]

Choi, S. S.

Christofferson, J.

Coker, J. G.

M. R. Hee, C. A. Puliafito, J. S. Duker, E. Reichel, J. G. Coker, J. R. Wilkins, J. S. Schuman, E. A. Swanson, and J. G. Fujimoto, “Topography of diabetic macular edema with optical coherence tomography,” Ophthalmology105(2), 360–370 (1998).
[CrossRef] [PubMed]

Cooper, R. F.

Dastmalchi, S.

C. Ahlers, C. Simader, W. Geitzenauer, G. Stock, P. Stetson, S. Dastmalchi, and U. Schmidt-Erfurth, “Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography,” Br. J. Ophthalmol.92(2), 197–203 (2008).
[CrossRef] [PubMed]

de Boer, J. F.

Díaz, M. E.

M. E. Díaz, G. Ayala, R. Sebastian, and L. Martínez-Costa, “Granulometric analysis of corneal endothelium specular images by using a germ-grain model,” Comput. Biol. Med.37(3), 364–375 (2007).
[CrossRef] [PubMed]

Dijkstra, E. W.

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik1(1), 269–271 (1959).
[CrossRef]

Dilley, J.

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F. J. Sanchez-Marin, “Automatic segmentation of contours of corneal cells,” Comput. Biol. Med.29(4), 243–258 (1999).
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A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging30(2), 484–496 (2011).
[CrossRef] [PubMed]

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C. Ahlers, C. Simader, W. Geitzenauer, G. Stock, P. Stetson, S. Dastmalchi, and U. Schmidt-Erfurth, “Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography,” Br. J. Ophthalmol.92(2), 197–203 (2008).
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Invest. Ophthalmol. Vis. Sci.

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Figures (8)

Fig. 1
Fig. 1

Schematic for segmenting closed-contour features via GTDP.

Fig. 2
Fig. 2

Schematic of the quasi-polar transform. (a-c) A circle, oval, and arbitrary shape in the Cartesian domain. (d-f) Transformation of (a-c) into the polar domain based on the centroid (yellow asterisks in a-c). The centroid in (a-c) becomes a line (yellow) in the polar domain. (g) Flattening of (d-f) into a line in the quasi-polar domain. The result is a transformation of any closed-contour shape in the Cartesian domain into a line in the quasi-polar domain.

Fig. 3
Fig. 3

RPE cell segmentation using the quasi-polar transform. (a, b) Image containing the cell to segment (a), and the pilot estimate (b) with its centroid (blue asterisk). (c) Polar-transformation of (b) segmented using GTDP to generate r=f(θ) (green). (d) Polar-transformation of (a) using the centroid from (b) as the reference pixel. The black regions are invalid points that lie outside the image in the Cartesian domain. (e, f) Images (c) and (d) flattened based on r=f(θ), respectively. (g) Transformation of r'=f(θ') from (f) back into the Cartesian domain.

Fig. 4
Fig. 4

Examples of RPE cell segmentation. (a) Automatically segmented confocal fluorescence image of a flat-mounted mouse retina (Image 16 in Table 1). The cell borders could still be segmented in cases when (b) the pilot estimate (yellow) was off-center and not a close estimate of the cell, (c) image artifacts or extraneous features were present, (d) the reflectivity of the cell border varied, (e) the cell had a low signal-to-noise ratio, (f) the cell was of abnormal shape, and (g) cell sizes were large or small. Each colored box in (a) corresponds to the zoomed-in image with the same colored box in (b-g).

Fig. 5
Fig. 5

Comparison of fully automatic segmentation versus the gold standard. (a, d) Confocal fluorescence microscopy images of flat-mounted mouse retina. (b, e) Gold standard segmentation of RPE cells (magenta) obtained semi-automatically using an independent technique. (c, f) Fully automatic segmentation (magenta) using the presented closed-contour GTDP technique. For the gold standard, cells bordering the image and invalid regions due to folding of tissue during preparation and imaging artifacts were ignored. These regions were therefore discarded (a-f, black borders) for the comparison study shown in Table 1.

Fig. 6
Fig. 6

Zoomed-in comparison of fully automatic segmentation versus the gold standard. (a) Erroneous gold standard segmentation: The small middle cell was merged with adjacent cells. (b) Erroneous gold standard segmentation that did not significantly alter quantitative comparison: Although the middle cell was incorrectly shifted, the cell count remained correct. (c) Erroneous gold standard segmentation: An enlarged diseased cell was incorrectly separated into two cells. We emphasize that such errors (a-c) were very infrequent in the gold standard data set consisting of thousands of semi-automatically segmented cells. However, existence of even a handful of such errors shows the limitation of subjective segmentation techniques relative to the automatic segmentation (f-h). (d, i) An undetermined case: The gold standard (d) delineated two separate cells, while the automatic segmentation (i) grouped them as a single cell. Since these are diseased RPE cells, it is unclear whether cells with a partially disappeared cell border should be considered individually or as a unit. (j) Erroneous automatic segmentation: Borders were incorrectly drawn through the cells due to an artifact. (e-h) Accurate gold standard (e) and automatic (f-h) segmentation.

Fig. 7
Fig. 7

(a) SDOCT retinal image of a patient with DME. (b) Fully automatically segmented retinal layers (inner limiting membrane – blue; RPE – magenta; Bruch’s membrane – cyan) via layer GTDP, and segmented intra-retinal cysts (magenta) via closed-contour GTDP.

Fig. 8
Fig. 8

(a) AOSO image of cone photoreceptors provided by the authors of [18]. (b) Fully automatically segmented cones using the closed-contour GTDP segmentation technique.

Tables (1)

Tables Icon

Table 1 Comparison of the RPE cell count and average cell area obtained for each confocal fluorescence microscopy image via fully automatic segmentation versus the gold standard

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

B c all (z)={ 1,if zpilot estimate 0,otherwise ,z
B p (r,θ)= B c (i,j), 0<rmax( (i i ref ) 2 + (j j ref ) 2 ),i,j 0°<θ360°, i=rsin(θ)+ i ref ,j=rcos(θ)+ j ref .
t q1 =bwlabel( t q >0), t q2 =bwlabel(~ t q | t q ==1), t q3 =2bwlabel( t q ~=1)2.
T q1 ( T q1 ==0)= T q2 ( T q2 >0)+1.5, T q3 ( T q3 <0)=max( T q3 )+2, T q = T q1 + T q3 .
w ab =normalize( I q ( z a ) I q ( z b ),0,0.25 ) +normalize( T q ( z a )+ T q ( z b ),1,2 )+ w min ,
p={ p 1 ,if  i=1 Θ I q ( p 1 (θ ' i ),θ ' i ) i=1 Θ I q ( p Θ (θ ' i ),θ ' i ) p Θ ,otherwise .

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