4.6. REMARKS 41

Figure 4.4: Illustration of the measure of overlap between two regions A and B.

detection is 53 pixels. The sensitivity of detection is calculated as the ratio of the number of images with successful detection of the ONH to the number of images analyzed.

The acceptable distance value of 60 pixels, which has been used by other researchers with the STARE dataset [16, 27, 31], when converted using the above-mentioned scale factor, yields a limit of 46 pixels for the images in the DRIVE dataset. In order to facilitate comparison with other published works with the same datasets but different criteria for successful detection, FROC curves were derived using all of the limits mentioned above.

4.6REMARKS

The DRIVE and STARE datasets used for evaluation of the methods were described in this chapter. An ophthalmologist (A.L.E.) marked the center and contour of the ONH in the images; the two datasets do not provide such information. The methods used for evaluation of the results obtained, including measures of distance and overlap as well as FROC, were described. The results obtained with the datasets and evaluation methods are presented in Chapter 5 and Chapter 6.

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C H A P T E R 5

Detection of the

Optic Nerve Head

Using the Hough Transform

The procedure designed and developed in the present work to detect the ONH using the Hough transform is summarized in the flowchart in Figure 5.1. Because the ONH usually appears as a circular region, an algorithm for the detection of circles may be expected to locate the ONH in a retinal fundus image [72]. In the present work, the Hough transform for the detection of circles is used. Before applying the Hough transform, methods for detecting edges are applied to obtain an edge map, which is used as the input to the Hough transform. The best-fitting circle for the ONH is chosen by using a method of intensity-based selection. The sections of the book that provide descriptions of the various steps are labeled in Figure 5.1. The step of selection of the best-fitting circular approximation of the ONH using the reference intensity is described in the present chapter.

5.1DERIVATION OF THE EDGE MAP

In the present work, the edge map is obtained by applying the Sobel or Canny method to the preprocessed image followed by thresholding, as described in Section 3.2. If the threshold is set too low, there will be a large number of nonzero pixels in the edge map, leading to more computation with the Hough transform. If the threshold is too high, there could be very few nonzero pixels to define an approximate circle in the ONH area, which could cause the detection method to fail. An optimal threshold may need to be determined for each dataset. For the images in the DRIVE dataset, the edge images obtained using the Sobel operators were binarized using a fixed threshold of 0.02; the threshold for the results of the Canny method was fixed at 0.17. For the images in the STARE dataset, the edge images obtained using the Sobel operators were binarized using a threshold chosen automatically by the MATLAB [58] version of the Sobel method; the threshold for the results of the Canny method was fixed at 0.17. The thresholds were chosen by experimentation with a number of images from the DRIVE and STARE datasets, involving comparison of the edge maps obtained at various thresholds with the blood vessels visible in the original images.

The edge maps obtained for the image 01 from the DRIVE dataset are shown in Figure 5.2. The edge maps for the image im0001 from the STARE dataset are shown in Figure 5.3. We can observe that, in the case of the DRIVE image in Figure 5.2, edges can be found around the