ANTERIOR CHAMBER ANGLE MEASUREMENT USING OPTICAL COHERENCE TOMOGRAPHY IMAGE

ANTERIOR CHAMBER ANGLE MEASUREMENT USING OPTICAL COHERENCE TOMOGRAPHY IMAGE M.Prabha Mr.A.Rajan ME-Applied Electronics Assistant Professor (Sr)/ECE Shri Andal Alagar College of Engineering Shri Andal Alagar College of Engineering Mamandur Mamandur Abstract-Optical Coherence Tomography (OCT) is an imaging modality that has several advantages (e.g. high resolution and three dimensional imaging) in comparison with other ophthalmic imaging methods. Angle-closure glaucoma is a major blinding eye disease and could be detected by measuring the anterior chamber angle in the human eyes. High-definition OCT (Cirrus HD-OCT) is an emerging noninvasive, high-speed, and high-resolution imaging modality for the anterior segment of the eye.in this paper we present an approach using morphological operators for the completely automated segmentation of the anterior chamber region, which is crucial for the further examination of the geometrical parameters that indicate the presence of glaucoma. Keywords: Angle-closure glaucoma, anterior chamber angle, HD-OCT. Introduction GLAUCOMA is one of the major blinding eye diseases globally. According to, it is the second leading cause of blindness after cataract and is the leading cause of irreversible visual loss. Glaucoma is largely caused by poor filtration of aqueous fluid in the eyeball through the anterior chamber angle (ACA). If untreated, it leads to higher intraocular pressure, permanent nerve damage, and blindness. There are two main types of primary glaucoma, depending on how the flow of fluid is blocked: 1) Open-angle glaucoma is caused by a gradual hype functioning of the trabecular meshwork. 2) Angle-closure glaucoma (ACG) is caused by structural occlusion of the angle by peripheral iris, as shown in Fig. Figure 1: Causes of Angle-closure glaucoma. Given that glaucoma is asymptomatic in the early stage and is often only recognized when the disease is quite advanced and vision is lost, the detection of ACG using clinical imaging modalities could aid in arresting its development or slow down the progression. Anterior chamber angle assessment is used for the detection of ACG and is essential in deciding whether or not to perform laser iridotomy. Three approaches, namely, gonioscopy, ultrasound bio microscopy (UBM), and anterior segment optical coherence tomography (AS-OCT), are used for visualizing and measuring the ACA. A. Gonioscopy The current reference standard for evaluation of the ACA is gonioscopy, which was introduced by Trantas in 1899. Though considered as gold standard, gonioscopy is highly subjective. The definition of angle findings varies across grading schemes, and there is no universal standard. It is also prone to potential measurement errors due to how the lens is placed on the eye and different illumination intensities. As such, there are severe constraints to its potential as a screening tool. B. Ultrasound Biomicroscopy (UBM) 13

An alternative approach for viewing ACA is ultrasound biomicroscopy (UBM), which uses a higher frequency transducer than regular ultrasound for more detailed assessment of the anterior ocular structures. Ishikawa et al. designed a semiautomated program (UBMPro2000) to calculate several important parameters, based on the manual identification of the scleral spur, which is prone to intraobserver and interobserver variability. Although UBM is useful in quantifying the ACA, the equipment is costly and the image resolution is sometimes unsatisfactory. Furthermore, it is neither user nor patient friendlyas a water bath is needed to image the eye. C. Anterior Segment Optical Coherence Tomography (ASOCT) Anterior segment optical coherence tomography (AS-OCT) is another instrument for imaging the anterior chamber angle. Optical coherence tomography is analogous to ultrasound imaging, as the image is formed by detecting the signal backscattered from different tissue structures. Instead of sound waves, light is used for OCT imaging, which avoids the need for direct contact with the eyes. Furthermore, the use of light achieves higher spatial resolution than ultrasound. From the experiments in, AS-OCT is found to be at least as sensitive in detecting angle closure when compared with gonioscopy. The existing angle assessment parameters used in AS-OCT are the same as UBM images. The current Visante built-in angle assessment software requires substantial user labelling, the scleral spur, cornea, and iris; hence the measurements are subjective. The Zhongshan Angle Assessment Program is able to define the borders of the corneal epithelium, endothelium, and iris to measure the ACA using the location of scleral spur as the only observer input. However, it is found that the scleral spur is not identified in 20% to 30% of Visante OCT images and measurements using the scleral spur as the landmark are subjective to intraobserver and interobserver variability. I. OPTICAL COHERENCE TOMOGRAPHY Optical coherence tomography (OCT) is a high resolution cross-sectional imaging modality initially developed for retinal imaging. Anterior segment OCT (ASOCT) imaging was first described in 1994 by Izatt et al using the same wavelength of light as retinal OCT, namely 830nm. This wavelength is suboptimal for imaging the angle due to limited penetration through scattering tissue such as the sclera. OCT imaging of the anterior segment with a longer wavelength of 1310nm was developed later on and had the advantages of better penetration through sclera as well as real-time imaging at 8 frames per second. Currently, there are two commercially produced dedicated anterior segment devices, the SL-OCT (Heidelberg Engineering) and the Visante (Carl Zeiss Meditec, Inc.), of which only the latter is available in the United States. With the development of Fourier domain OCT (FDOCT) technology, real-time imaging of the posterior segment has also become feasible. Several retinal FDOCT devices allow imaging of the anterior segment, however they still use the shorter wavelength of 830-870nm with its inherent disadvantages in imaging the AC angle. The higher resolution provided by FD retinal OCT devices does have advantages in imaging other structures in the anterior segment such as the cornea and conjunctiva. Fourier domain OCT devices operating at the longer wavelength suited for AC angle imaging have also been described, but these are not yet commercially available in the United States. Figure 2: Enhanced Anterior Segment The Visante OCT has several scanning protocols of which the Enhanced Anterior Segment Single Scan and the High Resolution Raw scan are most useful for angle assessment. Figure is an example of a good-quality Enhanced Anterior Segment Scan. The important features include good horizontal and vertical centration of the image within the frame, the presenceof a reflex saturation beam indicating perpendicularity of the eye to the scanning beam and minimal tilting of the image. The scan dimensions are 16 x 6 mm and 4 image frames are averaged in order to increase the signal to noise ratio. In this scan protocol, the image is automatically corrected for the effects of refraction of the scanning beam. The High Resolution Raw Scan samples 512 A scans/image (as opposed to 256 A scans/image for the Enhanced scan), there is no image averaging or refraction correction. 14

1.1 Qualitative assessment An important landmark to identify when interpreting ASOCT images is the scleral spur. This is visible as an inward projection of the sclera at the junction between the inner scleral and corneal curvatures. Apposition between the iris and the inner corneo-scleral wall has been used in several studies as a qualitative method of detecting angle closure, however it must be noted that the degree of apposition may be variable and does not correlate exactly with appositional closure as defined by gonioscopy. In addition, several studies have shown that the when using the Anterior Segment Scan protocol which does not utilize image averaging, the scleral spur is not visible in about 25% of cases in this situation, it is still possible to qualitatively assess irido-corneal apposition in most images. 1.2 Quantitative assessment Load Image RGB to GRAY Scale Conversion Median Filter Thersholding Erosion Hole Filling Figure 3: Anterior Segment with angle closure Quantitative measurement of the AC angle is possible with inbuilt software in most of the anterior segment devices and also requires identification of the scleral spur. Limitations in visibility of scleral spur and the wide natural variation in angle anatomy within the same eye as well as between eyes are limiting factors in the routine use of quantitative measurement for angle assessment. II. METHODOLOGY 2.1 Block Diagram The stages involved in the angle measurement of OCT image are discussed. It starts with a brief review of the block diagram processes involved. Cropping the Anterior Chamber Region Angle Calculation Figure 4: Block Diagram Representation 2.2 OCT IMAGE 15

ASOCT uses the principle of low-coherence interferometry instead of ultrasound to produce high-resolution, cross sectional images of the anterior segment of the eye. The technique measures the delay and intensity of the light reflected from the tissue structure being analysed and compares it with the light reflected by a reference mirror. The combination of these two signals results in interference phenomenon. The signal intensity depends on the optical properties of the tissues, and the device uses these signals to construct a sagittal cross-section image of the structure being analysed. OCT technology was initially used to produce images of the posterior segment of the eye by using a wavelength of 820nm. In 2001, the wavelength was altered to 1310nm to allow better penetration through light retaining tissues such as the sclera and limbus and to improve visualization of the anterior segment.compared with UBM, this technology provides a higher axial resolution (18um versus 25um in 50MHz UBM) and faster sampling rate (2.0 khz versus 0.8 khz). Another main clinical advantage over UBM is its ability to provide noncontact scanning in a seated, upright position. However, the image acquisition can be affected at times by the superior eyelid, and oblique angles may allow cross-sectional images. In addition, image distortions may result from off axis measurements, requiring special software correction to eliminate the influence of scanning angle and refractive index of the cornea. Lack of a coupling medium may affect the image quality due to abnormalities in the anterior surface of the eye. The major drawback for AS-OCT is its inability to visualize structures posterior to the iris due to blockage of wavelength by pigment. This limits its application in discerning several secondary causes of angle Closure, such as plateau iris, ciliary body cyst or tumor, lens subluxation, or ciliary effusions. The two AS- OCT devices commercially available are Visante-OCT and slit-lamp OCT Compared with the Visante-OCT, the SL-OCT has lower axial and transverse resolution, slower image acquisition, and requires manual rotation of the scanning beam. Figure 5: Anterior segment HD-OCT image. Angle recess the region between the cornea and iris. Scleral spur the point where the curvature of the angle wall changes, often appearing as an inward protrusion of sclera. Corneal Endothelium innermost layer of cornea. Corneal epithelium outer most layer of cornea. 2.3 RGB TO GRAY SCALE IMAGE Humans perceive color through wavelength-sensitive sensory cells called cones. There are three different types of cones, each with a different sensitivity to electromagnetic radiation (light) of different wavelength. One type of cone is mainly sensitive to red light, one to green light, and one to blue light. By emitting a controlled combination of these three basic colors (red, green and blue), and hence stimulate the three types of cones at will, we are able to generate almost any perceivable color. This is the reasoning behind why color images are often stored as three separate image matrices; one storing the amount of red (R) in each pixel, one the amount of green (G) and one the amount of blue (B). We call such color images as stored in an RGB format. In gray scale images, however, we do not differentiate how much we emit of the different colors. We emit the same amount in each channel. What we can differentiate is the total amount of emitted light for each pixel; little light gives dark pixels and much light is perceived as bright pixels. When converting an RGB image to gray scale, we have to take the RGB values for each pixel and make as output a single value reflecting the brightness of that pixel. One such approach is to take the average of the contribution from each channel: (R+B+C)/3. However, since the perceived brightness is often dominated by the green component, a different, more "human-oriented", method is to take a weighted average. For Example: 0.3R + 0.59G + 0.11B. 16

A different approach is to let the weights in our averaging be dependent on the actual image that we want to convert, i.e., be adaptive. A simple take on this is to form the weights so that the resulting image has pixels that have the most variance, since pixel variance is linked to the contrast of the image. In the applet above, the "optimal projection" calculates how we should combine the RGB channels in the selected image to make a gray scale image that has the most variance. Syntax: I = rgb2gray (RGB) It converts the true color image RGB to the grayscale intensity image I. rgb2gray converts RGB images to grayscale by eliminating the hue and saturation information while retaining the luminance. 2.4 MORPHOLOGICAL OPERATION Morphology is a broad set of image processing operations that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. By choosing the size and shape of the neighborhood, you can construct a morphological operation that is sensitive to specific shapes in the input image.the most basic morphological operations are dilation and erosion. Dilation adds pixels to the boundaries of objects in an image, while erosion removes pixels on object boundaries. The number of pixels added or removed from the objects in an image depends on the size and shape of the structuring element used to process the image. In the morphological dilation and erosion operations, the state of any given pixel in the output image is determined by applying a rule to the corresponding pixel and its neighbors in the input image. The rule used to process the pixels defines the operation as dilation or erosion. 2.4.1 Structural Element An essential part of the dilation and erosion operations is the structuring element used to probe the input image. A structuring element is a matrix consisting of only 0's and 1's that can have any arbitrary shape and size. The pixels with values of 1 define the neighborhood. Two-dimensional, or flat, structuring elements are typically much smaller than the image being processed. The center pixel of the structuring element, called the origin, identifies the pixel of interest the pixel being processed. The pixels in the structuring element containing 1's define the neighborhood of the structuring element. These pixels are also considered in dilation or erosion processing. Three-dimensional, or non-flat, structuring elements use 0's and 1's to define the extent of the structuring element in the x and y-planes and add height values to define the third dimension. 2.4.2 Dilation Dilation adds pixels to the boundaries of objects in an image. The value of the output pixel is the maximum value of all the pixels in the input pixel's neighborhood. One immediate advantage of the morphological approach over low pass filtering is that the morphological method resulted directly in a binary image, while low pass filtering started with producing gray-scale image. The dilation of A by the structuring element B is defined by: 2.4.3 Erosion Erosion is the opposite of the dilation. The value of the output pixel is the minimum value of all the pixels in the input pixel's neighborhood. In a binary image, if any of the pixels is set to 0, the output pixel is set to 0. The Erosion of A by the structuring element B is defined by: 2.4.4 Opening Opening generally smoothes the contour object, breaks narrow isthmuses, and eliminates thin protrusions. Opening decreases sizes of the small bright detail, with no appreciable effect on the darker gray levels, while the closing decreases sizes of the small dark details, with relatively little effect on bright features. Opening generally smoothes the contour object, breaks narrow isthmuses, and eliminates thin protrusions. The opening of A by B is obtained by the erosion of A by B, followed by dilation of the resulting image by B. 2.4.5 Closing Closing also tends to smooth sections of contours but, as opposed to opening, it generally fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour. Closing also tends to smooth sections of contours. 2.5 THRESHOLDING Thresholding is one of the most important approaches to image segmentation. From a grayscale image, thresholding can be used to create binary images. Segmentation is categorized as 1) Threshold based segmentation, 17

2) Edge based segmentation, 3) Region based segmentation, 4) Clustering techniques, 5) Matching. Threshold based segmentation: Histogram thresholding and slicing techniques are used to segment the image. They may be applied directly to an image, but can also be combined with pre- and post-processing techniques. Edge based segmentation: With this technique, detected edges in an image are assumed to represent object boundaries, and used to identify these objects. Region based segmentation: Where an edge based technique may attempt to find the object boundaries and then locate the object itself by filling them in, a region based technique takes the opposite approach, by (e.g.) starting in the middle of an object and then growing outward until it meets the object boundaries. Clustering techniques: Although clustering is sometimes used as a synonym for (agglomerative) segmentation techniques, we use it here to denote techniques that are primarily used in exploratory data analysis of high-dimensional measurement patterns. In this context, clustering methods attempt to group together patterns that are similar in some sense. This goal is very similar to what we are attempting to do when we segment an image, and indeed some clustering techniques can readily be applied for image segmentation. Matching: When we know what an object we wish to identify in an image (approximately) looks like, we can use this knowledge to locate the object in an image. This approach to segmentation is called matching. Figure 6: Input image 3.1.2 Gray scale converted image In here we are converting input image to gray scale image as shown in fig 7. Figure 7: Gray scale Image 3.1.3 Morphological Operation III. RESULT ANALYSIS 3.1.1 Input Image The normal fundus image is taken as input image with resolution of 565X584 pixels in Tagged Image File Format (.tif). Figure 8: Filter Image 18

Figure 9: Erod Image Figure 12: Complemented Image 4.1.4 Angle Measurement Figure 10: Eliminated Image Figure 13: Anterior Image Figure 11: Morphological Closed Image Figure 14: Boundaring Image 19

Figure 15: Final output IV. CONCLUSION Anterior segment OCT technology enables examiners to obtain detailed cross-sectional images of the ACA while avoiding contact with the globe. These images can be analysed qualitatively. As a result, it is a quick and easily tolerated procedure for the patient. It also is likely that there is less distortion of angle morphology due to lack of globe manipulation. In this paper, we have described an anterior chamber angle measurement in optical coherence tomography image. We have used morphological operation that could assess the anterior chamber angle in HD-OCT images fast (around 1 s) and accurately. As a future work, better segmentation methods could be employed to reduce the speckle noise and eliminate the image dependant threshold value. V. REFERENCES [1] Jing Tian*, Pina Marziliano, Mani Baskaran, Hong-Tym Wong, and Tin Aung Automatic Anterior Chamber Angle Assessment for HD-OCT Images IEEE Transaction on the Biomedical Engineering, Vol. 58, NO. 11, Nov 2011. [2] A. T. Broman and H. A. Quigley, The number of people with glaucoma worldwide in 2010 and 2020, Br. J. Ophthalmol., vol. 90, pp. 262 267, 2006. [3] A. Dellaport, Historical notes on gonioscopy, Surv. Ophthalmol., vol. 20, pp. 137 149, 1975. [4] D. S. Friedman and H. Mingguang, Anterior chamber angle assessment techniques, Surv. Ophthalmol., vol. 53, no. 3, pp. 250 273, 2007. [5] C. Pavlin, M. Sherar, and F. FS, Subsurface ultrasound microscopic imaging of the intact eye, Ophthalmology, vol. 97, no. 2, pp. 244 250, Feb. 1990. [6] H. Ishikawa, K. Esaki, L. JM, Y. Uji, and R. Ritch, Ultrasound biomicroscop dark room provocative testing: A quantitative method for estimatin anterior chamber angle width, Jpn. J. Ophthalmol., vol. 43, no. 6, pp. 526 534, Nov./Dec. 1999. [7] W. P. Nolan, J. L. See, P. T. Chew, D. S. Friedman, S. D. Smith, S. Radhakrishnan C. Zheng, P. J. Foster, and T. Aung, Detection of primary angle closure using anterior segment optical coherence tomography in asian eyes, Ophthalmology, vol. 114, no. 1, pp. 33 39, Jan. 2007. [8] J. W. Console, L. M. Sakata, T. Aung, D. S. F. Man, and M. He, Quantitative analysis of anterior segment optical coherence tomography images: The Zhongshan angle assessment program, Br. J. Ophthalmol., vol. 92, pp. 1612 1616, 2008. [9] Visante omni: A new dimension in anterior segment evaluation, Carl Zeiss Meditec, Inc., Zeiss, 2007. [10] Cirrus HD-OCTUserManual Addendum-Anterior Segment Imaging, Carl Zeiss Meditec, Inc., Zeiss, 2007. [11] H.-T. Wong, M. C. Lim, L. M. Sakata, H. T. Aung, N. Amerasinghe, D. S. Friedman, and T.Aung, High-definition optical coherence tomography imaging of the iridocorneal angle of the eye, Arch.Ophthalmol., vol. 127, no. 3, pp. 256 260, 2009. 20