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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 3 — Jan. 30, 2012
  • pp: 3353–3366
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Automated phase retardation oriented segmentation of chorio-scleral interface by polarization sensitive optical coherence tomography

Lian Duan, Masahiro Yamanari, and Yoshiaki Yasuno  »View Author Affiliations


Optics Express, Vol. 20, Issue 3, pp. 3353-3366 (2012)
http://dx.doi.org/10.1364/OE.20.003353


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Abstract

An automated chorio-scleral interface (CSI) detection algorithm based on polarization sensitive optical coherence tomography (PS-OCT) is presented. This algorithm employs a two-step scheme based on the phase retardation variation detected by PS-OCT. In the first step, a rough CSI segmentation is implemented to distinguish the choroid and sclera by using depth-oriented second derivative of the phase retardation. Second, the CSI is further finely defined as the intersection of lines fitted to the phase retardation in the choroid and sclera. This algorithm challenges the current back-scattering intensity based CSI segmentation approaches that are not fully based on anatomical and morphological evidence, and provides a rational segmentation method for the morphological investigation of the choroid. Applications of this algorithm are demonstrated on in vivo posterior images acquired by a PS-OCT system with 1-μm probe.

© 2012 OSA

1. Introduction

Optical coherence tomography (OCT) [12

12. D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991). [CrossRef] [PubMed]

, 13

13. A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, “Optical coherence tomography - principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003). [CrossRef]

], which provides noninvasive and cross-sectional images with micrometric resolution, has been a common imaging method in ophthalmology [14

14. M. Hee, J. Izatt, E. Swanson, D. Huang, J. Schuman, C. Lin, C. Puliafito, and J. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995). [CrossRef] [PubMed]

16

16. J. G. Fujimoto, W. Drexler, J. S. Schuman, and C. K. Hitzenberger, “Optical coherence tomography (OCT) in ophthalmology: Introduction,” Opt. Express 17, 3978–3979 (2009). [CrossRef] [PubMed]

]. The OCT technique has been dramatically improved in terms of imaging speed and resolution in the past two decades since its invention [17

17. T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, “Megahertz oct for ultrawide-field retinal imaging with a 1050nm fourier domain mode-locked laser,” Opt. Express 19, 3044–3062 (2011). [CrossRef] [PubMed]

, 18

18. W. Drexler, U. Morgner, F. X. Kärtner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography,” Opt. Lett. 24, 1221–1223 (1999). [CrossRef]

]. The branch of Fourier-domain OCT (FD-OCT), including spectral-domain OCT and swept-source OCT, provides up to 20MHz scanning speed [17

17. T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, “Megahertz oct for ultrawide-field retinal imaging with a 1050nm fourier domain mode-locked laser,” Opt. Express 19, 3044–3062 (2011). [CrossRef] [PubMed]

, 19

19. A. Fercher, C. Hitzenberger, G. Kamp, and S. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117, 43–48 (1995). [CrossRef]

], as well as enhanced sensitivity and signal to noise ratio (SNR) [20

20. R. Leitgeb, C. Hitzenberger, and A. Fercher, “Performance of fourier domain vs. time domain optical coherence tomography,” Opt. Express 11, 889–894 (2003). [CrossRef] [PubMed]

22

22. J. F. de Boer, B. Cense, B. H. Park, M. C. Pierce, G. J. Tearney, and B. E. Bouma, “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography,” Opt. Lett. 28, 2067–2069 (2003). [CrossRef] [PubMed]

]. The increased acquisition speed to time domain OCT (TD-OCT) allows repeated 2D imaging of the retina, providing the possibility of speckle reduction and SNR improvement by averaging OCT images. Recently, the enhanced depth imaging (EDI) OCT technique has been developed and utilized to study the cross-sectional structure and measure the thickness of the choroid [23

23. R. F. Spaide, H. Koizumi, and M. C. Pozonni, “Enhanced depth imaging spectral-domain optical coherence tomography,” Am. J. Ophthalmol. 146, 496–500 (2008). [CrossRef] [PubMed]

, 24

24. T. Fujiwara, Y. Imamura, R. Margolis, J. S. Slakter, and R. F. Spaide, “Enhanced depth imaging optical coherence tomography of the choroid in highly myopic eyes,” Am. J. Ophthalmol. 148, 445–450 (2009). [CrossRef] [PubMed]

]. Another approach to obtaining a choroid image using OCT, the application of a 1-μm wavelength probe, is rapidly being developed for its high penetration ability in the posterior segment of the eye [25

25. B. Považay, B. Hermann, A. Unterhuber, B. Hofer, H. Sattmann, F. Zeiler, J. E. Morgan, C. Falkner-Radler, C. Glittenberg, S. Blinder, and W. Drexler, “Three-dimensional optical coherence tomography at 1050Mm versus 800Mm in retinal pathologies: enhanced performance and choroidal penetration in cataract patients,” J. Biomed. Opt. 12, 041211 (2007). [CrossRef]

29

29. Y. Yasuno, M. Miura, K. Kawana, S. Makita, M. Sato, F. Okamoto, M. Yamanari, T. Iwasaki, T. Yatagai, and T. Oshika, “Visualization of sub-retinal pigment epithelium morphologies of exudative macular diseases by high-penetration optical coherence tomography,” Invest. Ophth. Vis. Sci. 50, 405–413 (2009). [CrossRef]

].

Most retinal segmentation algorithms are achieved by using back-scattering intensity information obtained by conventional OCT [30

30. D. Cabrera DeBuc, “A review of algorithms for segmentation of retinal image data using optical coherence tomography,” in “Image Segmentation,” (InTech, 2011).

]. This intensity based segmentation relies on image contrast properties of each retinal layer, which have been widely investigated by comparing OCT images and histological studies. Various methods have been developed for the robust and fully automated segmentation of retinal OCT images. Hee et al. proposed the first segmentation method in TD-OCT based on intensity variation [31

31. M. R. Hee, “Optical coherence tomography of the eye,” Ph.D. thesis, Massachusetts Institute of Technology (1997).

]. Since then, various methods have been developed for the segmentation of OCT images. Recently reported automated segmentation can provide robust segmentation of several sub-layers in the retina with superior precision to manual segmentation [32

32. 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. Express 18, 19413–19428 (2010). [CrossRef] [PubMed]

, 33

33. I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in oct images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011). [CrossRef]

]. However, when it comes to the choroid, there is a lack of morphologic knowledge. Its thickness would become significantly reduced when cut off from its blood supply. This makes it difficult to acquire knowledge of the chorio-scleral interface (CSI) in morphology in vitro, and hence it has been impossible to define the CSI in an OCT image based on histological knowledge. Most of the current OCT studies about choroidal morphology are based on manual segmentation. Ophthalmologists manually and empirically identify CSI. However, to the best of our knowledge, no clear morphological or anatomical evidence supports the empirical manual segmentation of CSI. Recently, Kajić et al. reported an automated choroidal segmentation approach using a statistical model [34

34. V. Kajić, M. Esmaeelpour, B. Považay, D. Marshall, P. L. Rosin, and W. Drexler, “Automated choroidal segmentation of 1060 nm oct in healthy and pathologic eyes using a statistical model,” Biomed. Opt. Express 3, 86–103 (2012). [CrossRef]

], but the training data for the construction of this model was still obtained by manual segmentation of intensity OCT images.

Polarization sensitive OCT (PS-OCT) is a functional extension of OCT providing intensity tomography and birefringence tomography simultaneously [35

35. J. de Boer, T. Milner, and J. Nelson, “Determination of the depth-resolved Stokes parameters of light backscattered from turbid media by use of polarization-sensitive optical coherence tomography,” Opt. Lett. 24, 300–302 (1999). [CrossRef]

38

38. G. Yao and L. V. Wang, “Two-dimensional depth-resolved Mueller matrix characterization of biological tissue by optical coherence tomography,” Opt. Lett. 24, 537–539 (1999). [CrossRef]

]. Tissues consisting of organized microstructure or collagen alter the polarization status of light, reflected as a phase retardation change or other birefringent parameters. Several studies have reported retinal imaging using PS-OCT. The phase retardation and birefringence of the retinal nerve fiber layer has been well investigated [39

39. E. Götzinger, M. Pircher, and C. K. Hitzenberger, “High speed spectral domain polarization sensitive optical coherence tomography of the human retina,” Opt. Express 13, 10217–10229 (2005). [CrossRef] [PubMed]

, 40

40. M. Yamanari, M. Miura, S. Makita, T. Yatagai, and Y. Yasuno, “Phase retardation measurement of retinal nerve fiber layer by polarization-sensitive spectral-domain optical coherence tomography and scanning laser polarimetry,” J. Biomed. Opt. 13, 014013 (2008). [CrossRef] [PubMed]

]. Götzinger et al. reported a functional segmentation of RPE using depolarization information obtained by PS-OCT [41

41. E. Götzinger, M. Pircher, W. Geitzenauer, C. Ahlers, B. Baumann, S. Michels, U. Schmidt-Erfurth, and C. K. Hitzenberger, “Retinal pigment epithelium segmentation by polarization sensitive optical coherencetomography,” Opt. Express 16, 16410–16422 (2008). [CrossRef] [PubMed]

].

Birefringent properties of the choroid and sclera have a clear difference [39

39. E. Götzinger, M. Pircher, and C. K. Hitzenberger, “High speed spectral domain polarization sensitive optical coherence tomography of the human retina,” Opt. Express 13, 10217–10229 (2005). [CrossRef] [PubMed]

]. The sclera represents a strong birefringence because of its high concentration of of collagen, and hence the phase retardation should increase along the penetration. Meanwhile, despite of a small amount of collagenic components in the choroid, the choroidal birefringence is so low that it is negligible for the PS-OCT system with a typical birefringence sensitivity. And hence the phase retardation can be reasonably considered as constant in the choroid [42

42. M. Pircher, E. Götzinger, O. Findl, S. Michels, W. Geitzenauer, C. Leydolt, U. Schmidt-Erfurth, and C. K. Hitzenberger, “Human macula investigated in vivo with polarization-sensitive optical coherence tomography,” Invest. Ophth. Vis. Sci. 47, 5487–5494 (2006). [CrossRef]

]. This has been validated by high penetration PS-OCT using a 1-μm probe reported by Yamanari, et al. [43

43. M. Yamanari, Y. Lim, S. Makita, and Y. Yasuno, “Visualization of phase retardation of deep posterior eye by polarization-sensitive swept-sourceoptical coherence tomography with1-μm probe,” Opt. Express 17, 12385–12396 (2009). [CrossRef] [PubMed]

]. The depth-resolved birefringence properties measured by PS-OCT can be utilized as a contrast source for the segmentation of these tissues.

In this paper, we present an automated segmentation algorithm to detect the CSI based on phase retardation tomography obtained by PS-OCT. The segmentation algorithm consists of two steps: Firstly, a rough segmentation is achieved by model analysis and a dynamic programming algorithm in the phase retardation image to initialize further respective phase retardation analysis in choroid and sclera. Next, linear regressions are applied to both layers near the rough segmentation results, and the CSI is determined by the intersection of the two fitted lines. Finally, a back-scattering based error detection and correction algorithm is performed to avoid the segmentation error caused by large vessels. Several results of this algorithm are presented to verify the efficiency of CSI detection.

2. Methods

2.1. Acquisition of phase retardation image

In this study, we employed a full-range Jones matrix PS-OCT system with a 1-μm probe beam for the polarization sensitive measurement [44

44. M. Yamanari, S. Makita, Y. Lim, and Y Yasuno, “Full-range polarization-sensitiveswept-source optical coher-encetomography by simultaneous transversaland spectral modulation,” Opt. Express 18, 13964–13980 (2010). [CrossRef] [PubMed]

]. The principle and setup of the Jones matrix PS-OCT has been reported in detail elsewhere [43

43. M. Yamanari, Y. Lim, S. Makita, and Y. Yasuno, “Visualization of phase retardation of deep posterior eye by polarization-sensitive swept-sourceoptical coherence tomography with1-μm probe,” Opt. Express 17, 12385–12396 (2009). [CrossRef] [PubMed]

46

46. M. Yamanari, S. Makita, and Y. Yasuno, “Polarization-sensitive swept-source optical coherence tomography with continuous source polarization modulation,” Opt. Express 16, 5892–5906 (2008). [CrossRef] [PubMed]

]. In this system, the Jones matrix detection is achieved by modulating the incident light using an electro-optic modulator (EOM). It creates modulation-multiplexed two orthogonal polarization states. This polarization modulation results in two multiplexed OCT spectra with different carrier frequencies, i.e., a null-frequency and the same frequency with the polarization modulation. Both of the multiplexed spectra are then detected by polarization diversity detectors consisting of horizontal and vertical detectors. The OCT signals corresponding to the two carrier frequencies are numerically demultiplexed after detection. Since the OCT signals are multiplexed both by the carrier frequency and the polarization diversity detection, we finally obtain 4 OCT signals simultaneously. And then the cumulative Jones matrices of a sample are obtained by assigning the 4 OCT signals to each element of the Jones matrices. High-penetration Jones matrix tomography can be obtained from the posterior segment of eye by this PS-OCT system, and successive signal processing provides the corresponding phase retardation tomography.

Image quality is critical for biomedical image segmentation, especially for computer-assisted segmentation tasks. Speckle noise and a limited SNR are two of the main issues resulting in difficulty with segmentation. As reported in recent research, the signal to noise issue introduces both systematic and random errors in phase retardation measurement in the Jones matrix PS-OCT [47

47. S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010). [CrossRef] [PubMed]

]. Usually, the SNR of an optical signal back-scattered from the CSI is low. It results in randomness and a low contrast in phase retardation tomography. This prevents an accurate quantitative analysis of phase retardation, leading to the failure of phase retardation information based CSI segmentation. To improve the quality of the phase retardation images, we measured several B-scans repeatedly in a same position of the eye, and performed the Jones matrices averaging described in [48

48. Y. Lim, M. Yamanari, S. Fukuda, Y. Kaji, T. Kiuchi, M. Miura, T. Oshika, and Y. Yasuno, “Birefringence measurement of cornea and anterior segment by office-based polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 2, 2392–2402 (2011). [CrossRef] [PubMed]

].

Figure 1 shows intensity and phase retardation images resolved from a single Jones matrix B-scan and averaged Jones matrix B-scan. Figure 1(c) reveals that the averaging strongly reduced the speckle noise in intensity image and improves the SNR. It is also shown in Fig. 1(d) that an enhanced phase retardation contrast appears around the CSI.

Fig. 1 The efficiency of the Jones matrix averaging of 16 B-scans. (a) and (b) are the intensity image and phase retardation image extracted from single B-scan in PS-OCT, respectively. (c) and (d) are the intensity image and phase retardation image extracted from the average of 16 Jones matrix B-scans, respectively.

2.2. Two-step segmentation based on phase retardation

Since the sclera is a collagenous tissue and the choroid is not, the birefringence properties in choroid and sclera are quite different. In this work, the CSI is determined as the boundary between areas with different phase retardation properties. We use the gradient of phase retardation to represent the birefringence. The segmentation approach consists of two steps. A rough segmentation is implemented in advance for the initialization of subsequent phase retardation analysis. Then, depth-oriented linear regressions are applied to the phase retardations in both the roughly segmented choroid and the sclera for an exact segmentation.

2.2.1. Rough segmentation

The second step of our algorithm is based on the depth-oriented slope fitting of the phase retardation which is applied to the choroid and sclera separately. Therefore, the interface of these layers should be roughly identified in advance. The purpose of the rough segmentation from the first step is to determine the ranges of linear regressions for the second-step of our algorithm.

In our implementation, we first reduced the speckle noise using a rectangular averaging filter (size: 30 × 10 pix = 100 μm (lateral) × 79 μm (axial)) in the phase retardation images. This moving average significantly reduces the speckle in a phase retardation image as shown in Fig. 2(a). Then, the second derivative of the despeckled phase retardation was obtained along penetration in each A-line using the protocol described above. The distribution of the second derivative is shown in Fig. 2(b). A local maximum band can be observed around the expected CSI.

Fig. 2 Illustration of rough segmentation flow. (a) Speckle reduced phase retardation image. (b) Distribution of second derivative in the B-scan image. (c) The node cost distribution of potential CSI are masked on the intensity OCT image. The yellow line shows the segmented RPE/choroid interface. (d) Rough segmentation result is shown in red.

However, in Fig. 2(b), the CSI is not the only layer detected as local maximum. The strongest signal appears around the inner limiting membrane. To identify the CSI, the region of interest should be limited. This is achieved by segmentation of the choroid/RPE interface using the back scattering intensity information. The RPE complex is known as a set of hyper-reflective layers within retinal OCT images, while the choroid has a lower intensity signal than the RPE complex. Here we first adopt the same method as [49

49. S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006). [CrossRef] [PubMed]

] for RPE estimation. Then, the interface of the choroid and RPE is assigned to the pixels with minimum negative gradients beneath the RPE estimation in the intensity OCT image blurred by using a Gaussian filter with a standard deviation of 3 × 3 pixels. The yellow line in Fig. 2(c) indicates the RPE/choroid boundary segmented using this method. We shift this boundary 5 pixels (40 μm) towards the choroidal side to exclude the RPE from the region of interest. All of the pixels anterior to the choroid/RPE interface as well as other pixels with a negative second derivative value or an intensity lower than twice of noise floor are set to 0. Then, the second derivative information is normalized in each A-line. Here we use d(i, j) to denote the normalized second derivative of the j-th pixel in the i-th A-line.

To obtain a continuous curve as the CSI estimation, we applied a graph searching method using dynamic programming based on the second derivative information. The dynamic programming method has been used in several automated segmentations of retinal layers in intensity OCT images, providing a robust solution to shortest path or minimum cost problems without an initialization of start and end points [32

32. 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. Express 18, 19413–19428 (2010). [CrossRef] [PubMed]

, 50

50. M. Baroni, P. Fortunato, and A. L. Torre, “Towards quantitative analysis of retinal features in optical coherence tomography,” Med. Eng. Phys. 29, 432–441 (2007). [CrossRef]

, 51

51. Q. Yang, C. A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M. Araie, A. S. Raza, D. C. Hood, and K. Chan, “Automated layer segmentation of macular OCT images using dual-scale gradient information,” Opt. Express 18, 21293–21307 (2010). [CrossRef] [PubMed]

]. To apply the graph searching method, we first classify all pixels into either a potential CSI or a false CSI. The pixels meeting the condition of d(i, j) ≥ 0.5 are classified as potential CSI and the others are classified as false CSI. Furthermore, the node costs of these two types of pixels are assigned as
c(i,j)={2ford(i,j)<0.51d(i,j)ford(i,j)0.5.
(1)

The node cost of potential CSI slope is shown in Fig. 2(c) with a rainbow color-map superimposed on an intensity image, where transparent is assigned to the node cost of 2. In this definition, the node cost at a potential CSI ranges from 0 to 1, lower than half of the node cost in a false slope position. This setting can effectively limit the segmented CSI within the potential CSI band, isolating it from the fake patches of local maximum in the second derivative distribution.

The minimum cost from the first A-line to node (i, j) in this dynamic programming algorithm is represented as
cost(i,j)={c(i,j)i=0,0j<Mminj1nj+1cost(i1,n)+|nj|+1c(i,j)0<i<N,0j<Mothercases,
(2)
where M and N are the number of pixels in an A-line and the number of A-lines in a B-scan, respectively. |nj|+1 in the second case is a distance parameter that reflects the distance penalty in the graphic solution.

The optimal solution is defined by searching the path with minimum cost from the leftmost A-line to the rightmost A-line. A 50-pixel (3.3% of the transversal range) median filter is also applied to reject minor segmentation error. This solution is shown with a red line in Fig. 2(d).

2.2.2. Slope fitting in phase retardation

The gradient of phase retardation reveals birefringence properties in the tissue. Phase retardation increases rapidly with penetration in the sclera due to the presence of birefringent components, while remaining almost a constant in the choroid. The boundary of these two layers should appear as an inflection in the phase retardation model. The exact segmentation to the CSI is achieved by slopes fitting to cumulative phase retardation in the choroid and sclera as described in following paragraphs.

The phase retardation model in the choroid and sclera is illustrated in Fig. 3. In this model, we assumed constant phase retardation in the choroid. An average of the phase retardation is obtained between the RPE/choroid boundary and the initial estimation of the CSI for each Aline, where the RPE/choroid boundary was segmented by using the intensity image as described before, and the initial estimation of the CSI was obtained by the method described in 2.2.1. This averaging is equivalent to a linear regression to the phase retardation in the choroid by a regression line with zero. Linear regressions are applied to the 7-pixel (55-μm) regions in the sclera close to the initial estimation of the CSI. Since the initial segmentation might lack accuracy, we do 11 trials with the start point of this linear regression from −5 pixels to +5 pixels (−40 μm to +40 μm) to the initially estimated CSI, and select the regression with the maximum gradient as the phase retardation slope in the sclera. This operation is for excluding the choroidal and scleral regions with aliasing of the phase retardation from the linear regression since these two regions have low phase retardation that minimize the gradient. The CSI of each A-line is defined as the intersection of these two lines as shown in Fig. 3(a). Finally, the CSI is acquired by smoothing the intersections in the B-scan direction by a 50-pixel median filter as exemplified in Fig. 4(b), while Fig. 4(a) shows the CSI estimation before smoothing.

Fig. 3 Illustration of the slope fitting model in phase retardation. The black curve shows the phase retardation A-line signal marked with a white dashed line in Fig. 2(a) The blue and red dashed lines are the linear regression lines of phase retardation in the choroid and sclera, respectively. The CSI is determined by the intersection of these two lines.
Fig. 4 The CSI obtained by fitting the phase retardation model shown in Fig. 3(a). The red curve shows the intersections of the linear regression lines in each A-scan, (b) The CSI smoothed by a median filter.

2.3. Error correction

A large blood vessel in the choroid or sclera can disturb this phase retardation information based CSI detection. The anterior boundary of a blood vessel is sometimes detected as the CSI by this algorithm. One reason might be the unreliable measurement of the phase retardation in the blood vessels. The back scattering signal from blood is very weak, so the SNR inside of a blood vessel is relevantly low. As it was revealed in Ref. 47

47. S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010). [CrossRef] [PubMed]

, measured phase retardation would approach around 2/3 π as the effective SNR decreases. This erroneous high phase retardation would mimic the phase retardation in the sclera. The collagen in the vessel wall can also be a factor that misleads our phase retardation oriented CSI segmentation.

To eliminate the segmentation error around a large vein, an additional optimization algorithm based on an intensity image is applied. Identification of blood vessel’s position around the CSI is required in error correction. This is achieved by the analysis of intensity information beneath the CSI obtained in the two-step segmentation process described in Section 2.2. The intensity inside the blood is rather weak due to the low back scattering from blood. A moderate intensity can be observed in the sclera near the CSI, and the intensity constantly decreases along penetration in the intensity images acquired by PS-OCT. Note that this feature is only warranted in polarization-independent intensity OCT images, which are free from the birefringence artifact that exists in standard OCT images [26

26. Y. Yasuno, Y. Hong, S. Makita, M. Yamanari, M. Akiba, M. Miura, and T. Yatagai, “In vivo high-contrast imaging of deep posterior eye by 1-um swept source optical coherence tomography and scattering optical coherence angiography,” Opt. Express 15, 6121–6139 (2007). [CrossRef] [PubMed]

, 52

52. Y. Yasuno, M. Yamanari, K. Kawana, M. Miura, S. Fukuda, S. Makita, S. Sakai, and T. Oshika, “Visibility of trabecular meshwork by standard and polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 15, 061705 (2010). [CrossRef]

]. We distinguish the segmentation error by evaluating the distance between the segmented CSI and the pixel with maximum intensity beneath it in each A-line. If this distance is higher than a threshold, e.g. 5 pixels, the CSI would be corrected to the maximum intensity position, which indicates the posterior boundary of a blood vessel. In the end, a median filter with the width of 25 A-lines is utilized to reject the false correction that can happen in a single A-line.

Figure 5 shows an example of an intensity based segmentation error correction. As indicated by white arrows, the phase retardation based segmentation result is located within blood vessels in some regions. It is clear that the phase retardation failed in segmentation of the CSI. These errors can be detected and corrected by the intensity based process described above. The corrected segmentation result shown with a yellow line in Fig. 5 provides a more reasonable estimation of the CSI around the vessel regions.

Fig. 5 An example of intensity based segmentation error correction. The red and yellow lines denote the phase retardation based segmentation of the CSI and the intensity based correction result.

3. Results

We employed a 1-μm probe polarization sensitive swept source OCT to obtain phase retardation and back-scattering images. The setup and parameters of this system have been described in Ref. 43

43. M. Yamanari, Y. Lim, S. Makita, and Y. Yasuno, “Visualization of phase retardation of deep posterior eye by polarization-sensitive swept-sourceoptical coherence tomography with1-μm probe,” Opt. Express 17, 12385–12396 (2009). [CrossRef] [PubMed]

in detail. In vivo multiple B-scan imaging has been performed in healthy eyes. The macular region of the retina is imaged with 1,500 A-lines per frame and 64 frames are repeatedly acquired in a 5-mm horizontal area centered at the fovea. The probe power on the cornea was 0.81 mW.

The axial motion was detected and canceled by a custom-made correlation based algorithm. In this algorithm, a B-scan frame is selected as a reference. The cross-correlation functions between an A-line in the reference frame and the A-lines in the corresponding transversal location in the other frames are calculated. These correlation functions provide the axial displacement of each A-line respect to the reference frame. The outliers in the detected displacement within a frame were eliminated by applying medial filtering with a kernel size of 100 A-lines. The intra-frame-motion respect to the reference frame was then corrected by using the predicted displacement for each frame. After this motion correction, the image correlations between the reference frame and all of the motion corrected frames were calculated, and the most highly correlated 15 frames were selected. An averaged Jones matrix B-scan was yielded from the 15 frames and the reference frame by using the Jones matrix averaging algorithm [48

48. Y. Lim, M. Yamanari, S. Fukuda, Y. Kaji, T. Kiuchi, M. Miura, T. Oshika, and Y. Yasuno, “Birefringence measurement of cornea and anterior segment by office-based polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 2, 2392–2402 (2011). [CrossRef] [PubMed]

].

Six subjects without marked posterior disorder were involved in this study. Six eyes of three subjects were first measured. A high similarity between the two eyes of the same subject was observed. Hence, only one eye from each remaining subject was scanned. Finally, six eyes of the six subjects were involved in the following study.

An example of phase retardation based segmentation is shown in Fig. 6. This B-scan is acquired from a myopic eye of an adult subject. In the region indicated with an ellipse in Fig. 6(a), the tissue appears as a homogeneous intensity feature. No structrural information indicates the location of the CSI, so it is difficult to determine whether the CSI is smooth or abruptly convex in this region. In the same region in Fig. 6(b), a clear difference in birefringence property can be visualized through phase retardation information. The real CSI can be detected as shown in Fig. 6 using the 2-step algorithm described in Section 2.2. The phase retardation based method provides more reliable CSI segmentation than the intensity based method.

Fig. 6 PS-OCT images of the macular region. (a) Intensity image, (b) Phase retardation image. The white line in (b) shows the CSI segmentation by phase retardation based segmentation.

Fig. 7 A phase retardation based segmentation result is shown in intensity image (a) and phase retardation image (b). The yellow and black ellipses marked several unsmooth segments in the CSI.

Abnormal CSI segmentation associated with a low birefringence region beneath the fovea was sometimes obtained. Figure 8 shows an example. It is clear that there is a region with abruptly low phase retardation around the CSI near the fovea. This phase retardation distribution was found in three out of six subjects, either with myopia or hyperopia. Since this CSI segmentation algorithm is based on the phase retardation information, the segmented CSI can be given as a concave shape. However, a corresponding concave structure cannot be found in the intensity image (Fig. 8(a)). One potential reason for this could be the alteration of the birefringence property in the sclera at the foveal region. However, neither the phase retardation nor the intensity information can provide indisputable evidence to identify the CSI. Further study including an in vitro histological study may be required to correctly understand this issue.

Fig. 8 An example of segmentation in an eye with low birefringence in some scleral region. The red and white lines show the segmentation result in intensity image (a) and phase retardation image (b), respectively.

In measurements of one subject out of six, the penetration depth is quite limited in the choroid. The results are shown in Fig. 9. Both the intensity and phase retardation images are poor in the posterior choroidal region. Reasons for this might be a very thick choroid or strong light absorption. The low quality of phase retardation measurement leads to the failure of CSI segmentation. Even so, the algorithm still provided a reasonable CSI segmentation at the left part of the B-scan, i.e., at the nasal region. Further development and optimization of the PS-OCT hardware will provide higher signal intensity for these cases, and may solve this issue.

Fig. 9 Intensity (a) and phase retardation (b) B-scans acquired from an eye with poor visualization of the posterior choroidal region. The CSI segmentation results, a red line in (a) and a white line in (b), cannot represent the real CSI.

The repeatability of this method was also evaluated as follows. We first selected 16 B-scan frames from the 64 frames in a single dataset by the correlation based algorithm. An averaged Jones matrix image was created from these 16 frames. The RPE and the CSI were segmented from this averaged Jones matrix image, and the choroidal thickness distribution was defined as the distance between the RPE and the CSI. And then, another 16 frames were selected from the residual 48 frames by the same correlation algorithm, and the same operations including the averaging, segmentation, and calculation of choroidal thickness were performed. Namely, the segmentation was performed twice with two independent OCT images corresponding to the same location of the eye. Finally, the standard deviation of the difference of the choroidal thicknesses along the transversal direction was obtained. This standard deviation would provide a measure of repeatability of the segmentation algorithm.

We performed this evaluation with 4 datasets obtained from 4 subjects which show reasonable phase retardation distributions. The standard deviations of the difference of the choroidal thickness were 14.1 μm, 17.1 μm, 10.8 μm and 8.2 μm. These standard deviations correspond to 1- to 2-pixel depth of our PS-OCT image. And hence, the repeatability of our system is believed to be reasonable.

4. Discussion

In the Jones matrix PS-OCT, both the accuracy and precision of phase retardation measurement rely on effective SNR [47

47. S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010). [CrossRef] [PubMed]

]. An effective SNR is mainly determined by the lowest SNR channels in the Jones matrix measurement. In our system, two of the four channels use a phase modulated probe beam achieved by an electro-optic modulator. The SNR in the modulated channels are more than 10-dB lower than that in non-modulated channels. Hence, the effective SNR level is limited to a relatively low range, raising both systematic and random error in the phase retardation measurement. This is one of the main issues that degrade phase retardation analysis and segmentation. We believe optimization of the PS-OCT system can promote segmentation accuracy and reduce the failure rate of the segmentation.

Although the choroid is a phase retardation preserving layer, a moderate increase in phase retardation was sometimes observed in the choroid along the depth. This could be because of a systematic error caused by the decreasing effective SNR. A Monte-Carlo-based phase retardation estimator can restrain systematic error introduced by noise [57

57. L. Duan, S. Makita, M. Yamanari, Y. Lim, and Y. Yasuno, “Monte-carlo-based phase retardation estimator for polarization sensitive optical coherence tomography,” Opt. Express 19, 16330–16345 (2011). [CrossRef] [PubMed]

]. However, this method requires an accurate effective SNR value for each pixel. The Jones matrix averaging is a complex averaging process. Although the Jones matrices have a non-correlated global phase to each other, the global-phase is cancelled before the complex averaging [48

48. Y. Lim, M. Yamanari, S. Fukuda, Y. Kaji, T. Kiuchi, M. Miura, T. Oshika, and Y. Yasuno, “Birefringence measurement of cornea and anterior segment by office-based polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 2, 2392–2402 (2011). [CrossRef] [PubMed]

], a small amount of residual global phase results in an out-of-phase summation of the signals and degrades the effective SNR. Since this signal degradation is not fully predictable, the Monte-Carlo-based phase retardation estimator can not always provide a correct estimation of the phase retardation. Therefore, we did not apply the Monte-Carlo-Based estimator in this study. Further optimization of PS-OCT hardware will improve the sensitivity, and will eliminate the necessity of Jones matrix averaging. The Monte-Carlo-Based estimator could be a powerful aid to phase retardation based CSI segmentation for an improved future version of PS-OCT.

In current status, an image with 1,500 (lateral) × 300 (axial) pixels requires around 12 seconds for the pre-processing and 10 seconds for segmentation with an algorithm implementation written in LabVIEW (LabVIEW 2011 for 64-bit Windows 7) on Intel CORE i7 CPU Q720 at 1.60 GHz with 8-GB RAM. The pre-processing includes motion cancellation, Jones matrix averaging, and phase retardation calculation, and the time consumptions are nearly equally distributed in these three processes. Among the sub-processes in the segmentation process, the rough segmentation is the most time consuming process, it takes around 7 seconds. We expect to shorten the pre-processing time by taking the advantage of a graphics processing unit (GPU) in the future, since the pre-processing can be heavily parallelized according to its mathematical properties. Although we are currently using a multi-core CPU, the program has not been well parallelized. The segmentation speed can also be optimized by proper usage of multiple CUP cores.

5. Conclusion

Acknowledgments

This study is partially supported by the Japan Science and Technology Agency through the contract of the development program of advanced measurement systems.

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C. W. Spraul, G. E. Lang, and H. E. Grossniklaus, “Morphometric analysis of the choroid, bruch’s membrane, and retinal pigment epithelium in eyes with age-related macular degeneration.” Invest. Ophth. Vis. Sci. 37, 2724–2735 (1996).

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F. John V, “In vivo near-infrared fluorescence imaging,” Curr. Opin. Chem. Biol. 7, 626–634 (2003). [CrossRef]

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D. R. Guyer, L. A. Yannuzzi, J. S. Slakter, J. A. Sorenson, M. Hope-Ross, and D. R. Orlock, “Digital indocyanine-green videoangiography of occult choroidal neovascularization,” Ophthalmology 101, 1727–1735; discussion 1735–1737 (1994). [PubMed] .

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52.

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53.

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54.

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55.

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56.

M. Pircher, E. Götzinger, H. Sattmann, R. A. Leitgeb, and C. K. Hitzenberger, “In vivo investigation of human cone photoreceptors with slo/oct in combination with 3d motion correction on a cellular level,” Opt. Express 18, 13935–13944 (2010). [CrossRef] [PubMed]

57.

L. Duan, S. Makita, M. Yamanari, Y. Lim, and Y. Yasuno, “Monte-carlo-based phase retardation estimator for polarization sensitive optical coherence tomography,” Opt. Express 19, 16330–16345 (2011). [CrossRef] [PubMed]

OCIS Codes
(100.2960) Image processing : Image analysis
(110.4500) Imaging systems : Optical coherence tomography
(170.3890) Medical optics and biotechnology : Medical optics instrumentation
(170.4470) Medical optics and biotechnology : Ophthalmology
(170.4500) Medical optics and biotechnology : Optical coherence tomography

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: December 22, 2011
Revised Manuscript: January 18, 2012
Manuscript Accepted: January 19, 2012
Published: January 27, 2012

Virtual Issues
Vol. 7, Iss. 3 Virtual Journal for Biomedical Optics

Citation
Lian Duan, Masahiro Yamanari, and Yoshiaki Yasuno, "Automated phase retardation oriented segmentation of chorio-scleral interface by polarization sensitive optical coherence tomography," Opt. Express 20, 3353-3366 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-3-3353


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References

  1. R. A. Linsenmeier and L. Padnick-Silver, “Metabolic dependence of photoreceptors on the choroid in the normal and detached retina,” Invest. Ophth. Vis. Sci.41, 3117–3123 (2000).
  2. D.-Y. Yu and S. J. Cringle, “Oxygen distribution and consumption within the retina in vascularised and avascular retinas and in animal models of retinal disease,” Prog. Retin. Eye Res.20, 175–208 (2001). [CrossRef] [PubMed]
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  4. J. W. Kiel and W. A. van Heuven, “Ocular perfusion pressure and choroidal blood flow in the rabbit.” Invest. Ophth. Vis. Sci.36, 579–585 (1995).
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  7. C. W. Spraul, G. E. Lang, and H. E. Grossniklaus, “Morphometric analysis of the choroid, bruch’s membrane, and retinal pigment epithelium in eyes with age-related macular degeneration.” Invest. Ophth. Vis. Sci.37, 2724–2735 (1996).
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  14. M. Hee, J. Izatt, E. Swanson, D. Huang, J. Schuman, C. Lin, C. Puliafito, and J. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol.113, 325–332 (1995). [CrossRef] [PubMed]
  15. J. A. Izatt, M. R. Hee, E. A. Swanson, C. P. Lin, D. Huang, J. S. Schuman, C. A. Puliafito, and J. G. Fujimoto, “Micrometer-scale resolution imaging of the anterior eye in vivo with optical coherence tomography,” Archives of Ophthalmology112, 1584–1589 (1994). . [PubMed]
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  20. R. Leitgeb, C. Hitzenberger, and A. Fercher, “Performance of fourier domain vs. time domain optical coherence tomography,” Opt. Express11, 889–894 (2003). [CrossRef] [PubMed]
  21. M. Choma, M. Sarunic, C. Yang, and J. Izatt, “Sensitivity advantage of swept source and fourier domain optical coherence tomography,” Opt. Express11, 2183–2189 (2003). [CrossRef] [PubMed]
  22. J. F. de Boer, B. Cense, B. H. Park, M. C. Pierce, G. J. Tearney, and B. E. Bouma, “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography,” Opt. Lett.28, 2067–2069 (2003). [CrossRef] [PubMed]
  23. R. F. Spaide, H. Koizumi, and M. C. Pozonni, “Enhanced depth imaging spectral-domain optical coherence tomography,” Am. J. Ophthalmol.146, 496–500 (2008). [CrossRef] [PubMed]
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