CHAPTER 3 RETINAL OPTIC DISC SEGMENTATION

Transcription

1 60 CHAPTER 3 RETINAL OPTIC DISC SEGMENTATION 3.1 IMPORTANCE OF OPTIC DISC Ocular fundus images provide information about ophthalmic, retinal and even systemic diseases such as hypertension, diabetes, macular degeneration and arteriosclerosis. Detection of normal and abnormal features in retinal images is fundamental and helpful for automatic understanding of these images. Precise localization of the optic disc boundary is an important sub-problem of higher level problems in ophthalmic image processing. Optic disc is a bright yellowish disc with a whitish central cupping through which the central retinal artery and vein pass. Location of optic disc is an important issue in retinal image analysis as it is a significant landmark feature to locate anatomical components in retinal images for vessel tracking and for registering changes within the OD region. Localizing the disc is often necessary to differentiate the disc from other features of the retina and it is a prerequisite for computation of some important diagnostic indices for hypertensive retinopathy based on vasculature. Identification of outer boundary of the optic disc may allow ophthalmologists to quantitatively assess changes in the optic disc over time. OD segmentation is fundamental for establishing a frame of reference within the retinal image and is thus important for any image analysis application. Segmentation is also relevant for automated diagnosis of other ophthalmic pathologies and usually refers to the subsequent task of

2 61 determining the contour of the disc. An important information in digital fundus images is to differentiate between left and right eye. Automated localization of the optic nerve head is particularly important in making a diagnosis of glaucoma because the main symptoms in these cases are the links between the optic nerve, cupping parameters and differences in symmetry between the eyes. This chapter is organized as follows. In section 3.2, an automatic retinal optic disc segmentation algorithm using Differential Windowing (DW) technique in the polar coordinate domain is described. Section 3.3 describes the experimental set up used in the detection of OD. Experimental results for disc boundary extraction are presented in section 3.4. In section 3.5, results of the technique are compared with existing methods in terms of accuracy for few images. 3.2 PROPOSED DW OPTIC DISC SEGMENTATION ALGORITHM Anatomical structures of fundus images include optic disc, optic cup, blood vessels, macula and fovea. Change in the shape, color or depth of the optic disc is an indicator of various ophthalmic pathologies especially for glaucoma. Although OD has well defined features and characteristics, localizing the optic disc automatically and in a robust manner is not a straight forward process, since the appearance of OD may vary significantly due to retinal diseases and the disc size also varies from one person to another. Detection is critical due to the geometric relationship that exists between the vasculature and the position of the optic disc in the retina. Optic disc detection remains a problematic task due to hue changes within the optic disc boundary. OD segmentation is difficult since some parts of the disc boundary are not well defined and some parts are partly obscured by the

3 62 blood vessels in the retinal image which makes the disc shape more complicated. General purpose algorithms often fail to segment the optic disc due to fuzzy boundaries, inconsistent image contrast or missing edge features. Disc boundary detection is therefore aimed to correctly segment the OD by detecting the boundary between the retina and the nerve head. Flow diagram of the proposed DW disc segmentation algorithm is shown in Figure 3.1. Input image Mask generation Optic cup point detection ROI circle localization Left or right eye Blood vessel erasure Cartesian to Polar conversion Optic disc boundary Polar to Cartesian conversion Polygon closing Figure 3.1 Flow diagram to segment optic disc

4 63 Differential Windowing technique is a combination of edge detection and local maxima finding. As edge detection involves difference operation and maxima finding refers to identifying the window (region) with maximum value, the technique is known as differential window. OD boundary is detected using the following steps. Color fundus image shown in Figure 3.2 is used as the input image. Figure 3.2 Input image Mask Generation Mask generation aims to label the pixels belonging to the circular retinal fundus region in the entire image and exclude the background of the image from further calculations and processing. (a) (b) Figure 3.3 Mask generation

5 64 a) Retinal images are acquired in Red Green Blue (RGB) mode by fundus camera. Green plane is considered for OD extraction since it provides better contrast between the optic disc and the retinal tissue. Region with intensity greater than 5% of the average intensity in the green plane is selected. As optic disc is the brightest anatomical structure in a retinal image, the highest intensity pixels that contain areas in the optic disc are chosen. A binary mask shown in Figure 3.3(a) is generated initially and it is the result of thresholding operation. b) Erosion is performed with a circular window of radius equal to half the optic disc width. This operation will remove the isolated pixels with high intensity. Figure 3.3(b) is the result of erosion operation (circular window) done on the threshold image. Figure 3.3(b) represents a mask used for the Region of Interest (ROI). This mask is used since the disc region will not be present at the edges of the mask. Image Preprocessing Preprocessing highlights the optic disc region from the background region. c) Image opening is then performed with a circular window of radius equal to the optic disc width and it is shown in Figure 3.4(a). d) Opened image subtracted from the input image is shown in Figure 3.4(b). (a) (b) Figure 3.4 Image preprocessing

6 65 Optic cup point detection e) Average filtering is done with a circular window of radius 5% of the disc width to cover the entire optic disc region as shown in Figure 3.5(a). f) Maximum value point that gives the optic cup point is shown in Figure 3.5(b) (a) (b) Figure 3.5 Optic cup point detection ROI Circle Localization The circle parameters are set and the Region of Interest (ROI) is clipped as the bounding rectangle of the circle. ROI circle radius is set as 1.5 times the disc width parameter as in Figure 3.6(a) and ROI region is shown in Figure 3.6(b). (a) (b) Figure 3.6 ROI circle localization

7 66 Left or Right Eye Identification g) Blood vessels are extracted in the ROI. If blood vessels converge to the left, it is left eye. Else, it is right eye. i. Image closing is done with optic disc width. ii. Image subtraction of the closed image from the input image is performed. iii. In the subtracted image, if the mean value of left half image is greater (smaller) than the right half, then it is left (right) eye as shown in Figures 3.7(a) and 3.7(b). (a) Left Eye (b) Right Eye Figure 3.7 Identification of left and right eye For a left (right) eye, the blood vessel convergence happens to the left (right) side of the detected optic cup point. The direction of theta is set. iv. For left eye, theta is measured in counter clock-wise direction. v. For right eye, theta is measured in clock-wise direction as given in Figure 3.8.

8 67 Figure 3.8 Direction of theta Blood Vessel Erasure Dilation is performed followed by erosion operation in the ROI image. A circular window of maximum vessel width as radius is used for dilation and erosion. The impact of blood vessels in the optic disc and cup region is removed using morphological operations as in Figure 3.9. Figure 3.9 Blood vessel erasure Cartesian to Polar Conversion The circle with optic cup point as center and 1.5 times the disc width as radius is converted from cartesian to polar coordinate system along the positive theta direction as shown in Figure In the polar coordinate system, the radial and angular resolution in terms of the disc width is given as radial resolution = 1.5 disc width (3.1) angular resolution = 3 disc width (3.2)

9 68 Figure 3.10 Image representation in Cartesian to Polar form Figure 3.11 Cartesian to Polar conversion In Figure 3.10, (x-axis) represents the angular shift of the pixels under study from the horizontal direction and R (y-axis) represents the distance of the pixels under study from the detected optic cup point. Figure 3.11 describes the experimental result obtained during the image conversion. Optic disc boundary extraction Left and right eyes are identified using the optic cup point. Differential windowing (DW) technique is performed in the polar converted image and it is represented as in Figure Figure 3.12 Representation of DW technique

10 69 In order to identify the first point on the OD boundary, zero degree axis is chosen initially as there are no blood vessel interruption along this axis. Once the first point is identified, tracing of further points becomes easier. Here x represents the columns (horizontal direction) and y represents the rows (vertical direction). To obtain the OD boundary, window regions of size r X c are analyzed at the upper side and lower side of the search pixel. I Lower (x,y) represents mean intensity of a small window below the detected optic disc boundary in the previous iteration. I Upper (x,y) represents mean intensity of a small window above the detected optic disc boundary in the previous iteration. Aim is to find the boundary that maximizes the difference between I Upper (x,y) and I Lower (x,y) as represented in Figure In each iteration, the windows for I Lower (x,y) and I Upper (x,y) progresses in the forward direction of theta. The window size used is r X (2c + 1) and w is the increment in the horizontal direction for each new point. (2m + 1) represents the search space in the vertical direction. Totally 21 pixels (m=10) are searched with the centre of the search space as the detected boundary point from the previous column (previous theta value).this is done as the new boundary point of the current column will not vary far away from the previous boundary point of previous column. From the initial boundary point, DW operation is done at each degrees of the radial axis to obtain the OD boundary at each column of the polar converted image. If the i th point on the optic disc boundary has coordinates (x i, y i ), the (i+1) th point, (x i+1, y i+1 ), is computed iteratively as x = x + w (3.3) y = arg [ max I (x, y) I (x, y) ] (3.4)

11 70 (3.5) (3.6) OD is characterized by high intensity pixels. It has bright pixels on one side and less bright pixels on the other side. In the search space of 21 pixels, pixels with high intensity value has to be found. So the difference between average value of the upper window and lower window is found. Maximum of this difference value contributes to the disc boundary point and a point is obtained for each column as shown in Figure w is the increment in the direction for each new point. The column gets updated, x i becomes x i +w and the row depends on the pixel. Procedure is repeated for 1, 2 360, to get the entire boundary region. Figure 3.13 OD boundary extraction Polar to Cartesian Conversion In the fundus image transformed to a polar coordinate, image columns and rows correspond to angle and distance from the center of the optic disc respectively. After polar processing, the resultant image may then be transformed back to rectangular coordinates as shown in Figure 3.14 to generate a closed contour and to retain the rectangular shape of the image so that accurate measurements can be done on the identified features. Figure 3.14 Polar to Cartesian conversion

12 71 Polygon Closing Contour boundary detected in the polar coordinate system need not be a continuous closed boundary, when converted back to cartesian coordinate system. So, to create connected closed boundary, polygon closing is performed. The closed polygon in Figure 3.15 represents the optic disc boundary. Figure 3.15 Polygon closing 3.3 EXPERIMENTAL SETUP Algorithm tested on the database images from Digital Retinal Images for Vessel Extraction (DRIVE) database is given in Table 3.1. Images were acquired using a Canon CR5 non mydriatic 3 CCD camera with a 45 FOV. Each image was captured using 8 bits per color plane at 768 X 584 pixels. The FOV of each image is circular with a diameter of approximately 540 pixels. The images used in this technique are hand labeled by the observers trained by the ophthalmologists. Using the proposed technique, the optic disc was localized correctly in all the 40 images and the contour of the optic disc was found accurately. Though the contrast of the image is too low in few of the images, boundary of the optic disc was detected correctly. The proposed algorithm was developed in Matlab 7.8 Image processing toolbox. Table 3.1 lists the values of the different primary and derived parameters used in the proposed algorithm.

13 72 Table 3.1 Parameters used in the disc segmentation Parameter Value in Pixels Disc Width 200 Maximum Vessel Width 25 Average filtering Window radius 10 ROI Circle Radius 300 Blood Vessel Erasure: Window radius 25 Polar Coordinate: Radial Resolution 300 Polar Coordinate: Angular Resolution 1885 Differential filtering: search space parameter m 10 Differential filtering: window size parameter r (rows) 10 Differential filtering: window size parameter c (columns) 25 Differential filtering: Horizontal increment w EXPERIMENTAL RESULTS For a left eye, the blood vessel converges to the left side of the detected optic cup point as shown in Figure Figure 3.16 Few sample results for optic cup point detection in left eye

14 73 For a right eye, the blood vessel converges to the right side of the detected optic cup point as shown in Figure Figure 3.17 Few sample results for optic cup point detection in right eye in Figure Sample results of disc boundary extraction for 28 images are shown

15 74 Figure 3.18 Few sample results for optic disc boundary detection 3.5 PERFORMANCE ANALYSIS OF OPTIC DISC BOUNDARY DETECTION The proposed method was tested on a dataset which includes normal and abnormal retinal images collected from Aravind eye hospital, Madurai. Fundus images used in this work are captured by Topcon TRC50 EX mydriatic fundus camera under a fixed protocol with a 50 field of view, centered on the optic disc with a dimension of 1900 x1600 pixels at 24 bits true color images. Table 3.2 shows an accuracy measure for boundary localization for thirty real time retinal images. For each image ground truth was collected from two glaucoma experts and to compensate for inter observer

16 75 marking variations, an average boundary is obtained for each image by averaging the boundaries from the two experts. The optic disc boundary manually marked by an experienced ophthalmologist is set to be ground truth. Table 3.2 Accuracy for optic disc boundary detection Image ( ) (G ) Accuracy (%) value in pixels value in pixels A

17 76 An effective measure to evaluate the accuracy of detected optic disc boundary is calculated using the Equation (3.7). A = n ( D) n( D) (3.7) G corresponds to the pixels in the ground truth and D refers to the pixels in the detected optic disc region and n the no of pixels in the region. Assuming ideal case, the detected boundary region D will exactly match with the ground truth boundary G. In this case A becomes 1, and percentage becomes 100. As the detected boundary deviates from the ground truth, A value decreases from 100. The numerator in Equation (3.7) is an indicator of degree of correctness of detected boundary and denominator is an indicator of degree of incorrectness of detected boundary. As the inaccuracy in detected boundary increases, the numerator decreases and denominator increases, thus the overall value A decreases. Thus Equation (3.7) is a good indicator of boundary mismatch between ground truth boundary and detected boundary. Optic disc boundary obtained using DW technique is experimented for 30 real time images and an average accuracy of 97.9 % is achieved. STARE, DRIVE, DIARETDB0, DIARETDB1 are the four publicly available databases used to evaluate the accuracy of the proposed technique. Databases are a) STARE Database (81 images, 605 X 700 pixels) b) DRIVE Database (40 images, 565 X 584 pixels) c) Standard Diabetic Retinopathy Database Calibration Level 0 (DIARETDB0) (130 images, 1500 X 1152 pixels) and d) Standard Diabetic Retinopathy Database Calibration Level 1 (DIARETDB1) (89 images,1500 X 1152 pixels). A comparison of the windowing technique with Maximum Local Variation Method (MLVM), Hough Transform (HT) and Gradient Vector Flow (GVF) snake method in terms of boundary accuracy are shown in Table 3.3.

18 77 Table 3.3 Comparison of average accuracy for detecting optic disc boundary Database No of images MLVM HT GVF DW Accuracy (%) STARE DRIVE DIARETDB DIARETDB Average accuracy for 340 images Accuracy of OD boundary detected using DW technique are compared with the results of MLVM, HT and GVF snake method referred from Giri Babu Kande et al (2009) and are shown from Figure 3.19 to Figure Accuracy(%) Accuracy 0 MLVM HT GVF DW Images Figure 3.19 OD boundary detection for STARE database

19 78 Accuracy(%) Accuracy 90 MLVM HT GVF DW Images Figure 3.20 OD boundary detection for DRIVE database Accuracy(%) MLVM HT GVF DW Images Accuracy Figure 3.21 OD boundary detection for DIARETDB0 database

20 Accuracy(%) Accuracy 75 MLVM HT GVF DW Images Figure 3.22 OD boundary detection for DIARETDB1 database Accuracy(%) MLVM HT GVF DW 0 STARE DRIVE DIARETDB0 DIARETDB1 Images Figure 3.23 Comparison of average accuracy for four databases In order to evaluate the performance of DW technique, the results are compared with the state of results obtained from MLVM, HT and GVF technique as shown in Figure DW technique achieved a success rate of 98.7% in STARE, 100% in DRIVE, 97.7% in DIARETDB0 and 98.8% in

21 80 DIARETDB1 database. DW algorithm achieved an average success rate of 98.5%, that is the OD was correctly detected in 335 images out of 340 images tested, compared to an average accuracy of 89.7% in MLVM method, 87% in HT method and 94.4% in GVF snake method. DW method takes an average time of 5 seconds per image for computation. Classical segmentation algorithms like thresholding, edge detection and region growing techniques cannot find the optic disc boundary correctly as they do not incorporate the edge smoothness and continuity properties. Local variation technique described by Sinthanayothin et al (1999) used the variance of intensity between the adjacent pixels and the blood vessels and detected OD with a specificity of 99.1% and produces incorrect localization for fundus images with a large area of white lesions. In 2D circular HT, the dimensions of the normal circular histogram are reduced from 3 to 2 dimensions by assuming that the approximate OD radius is known. Computations in this technique depend on the number of edge pixels and the number of radii to be matched. During the whole process of finding the centre s and radii, circular Hough Transform has to be iterated each time with the different circles of different radii and some inaccuracies could occur. In the snake based approach, to fit active contour on to the optic disc, the initial contour must be placed near the optic disc boundary, otherwise it leads to wrong convergence. This method captures a range of shape and image variations, but the segmentation accuracy is sensitive to the contour initialization and had a difficulty in progressing into boundary concavities. In gradient vector flow snakes, contour is initialized either manually or automatically and deformation in the contour takes place under the influence of energy term defined on the image gradient or as a post processing step. Snakes initialized automatically with GVF as an external force has to choose various regularization parameters of GVF and snake parameters manually, to represent the smoothness and accuracy of the desired

22 81 boundary. Inspite of its robustness to initialization and increased range of capture, the method takes a long time to converge to object contours. In the proposed DW technique, representation of the images in polar coordinates facilitates the description of local image regions in terms of their radial and tangential characteristics to find a closed contour in the region of interest. The technique detects the boundary correctly though the disc is interrupted by strongly visible blood vessels. The method does not impose any constraints or lead to false positive and false negative points and hence provides an accurate boundary tracing with no loss of data. 3.6 SUMMARY Reliable and automated extraction of optic disc parameters can be a valuable diagnostic assisting resource for clinicians. Much of the prior work has focused on optic disc boundary detection, however the blood vessel occlusion problem has not been well solved. The proposed work has made a few contributions by proposing a novel approach to disc boundary detection which solves the problem of blood vessel occlusions. Optic disc segmentation in the polar coordinate domain gives significant results for the four databases and works with a high accuracy for the real time images. Hough transform has 3-dimensional parameter space such as (x, y, z) coordinate of the center of the ellipse, minor-axis radius and major-axis radius. A higher-dimensional parameter space HT is slow, because of the accumulator array updation with more parameters. Snake based methods for optic disc boundary extractions are slow because the computation of the snake boundary is an iterative process. The proposed method is non-iterative and computationally fast since it does not use any accumulator array. Methodologies for efficient automatic optic disc localization and left and right eye detection using DW technique have been presented in this work. Optic disc boundary was detected in 335 images and the success rate was found to be 98.5%. Left and right eye was

23 82 localized with 100% accuracy. DW algorithm works well even though the input retinal image is in a low contrast condition. The developed methodology has been checked to be independent and stable regardless of image resolution making it possible to work with poor resolution images. The main advantage of this method is that the OD s are detected even though the boundary of the optic disc is not continuous or blurred. When compared to the other approaches like MLVM, HT and GVF, DW technique can detect the disc boundary exactly even though the disc has many distracters. Edge detection and local maxima finding makes the proposed approach robust to blood vessel occlusions, ill defined edges, fuzzy shapes due to pathological changes and noises while maintaining the accuracy. Techniques employed in this system are capable of tracing boundaries of the optic disc sharply with exact contours and helps in improving the diagnostic accuracy thereby reducing the workload of ophthalmologists.