Fractal-based automatic localization and segmentation of optic disc in retinal images

A region growing and local adaptive thresholding-based optic disc detection

Automatic optic disc (OD) localization and segmentation is not a simple process as the OD appearance and size may significantly vary from person to person. This paper presents a novel approach for OD localization and segmentation which is fast as well as robust. In the proposed method, the image is first enhanced by de-hazing and then cropped around the OD region. The cropped image is converted to HSV domain and then V channel is used for OD detection. The vessels are extracted from the Green channel in the cropped region by multi-scale line detector and then removed by the Laplace Transform. Local adaptive thresholding and region growing are applied for binarization. Furthermore, two region properties, eccentricity, and area are then used to detect the true OD region. Finally, ellipse fitting is used to fill the region. Several datasets are used for testing the proposed method. Test results show that the accuracy and sensitivity of the proposed method are much higher than the existing state-of-the-art methods.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

Detection of the optic nerve head in fundus images of the retina using the Hough transform for circles

Detection of the optic nerve head (ONH) is a key preprocessing component in algorithms for the automatic extraction of the anatomical structures of the retina. We propose a method to automatically locate the ONH in fundus images of the retina. The method includes edge detection using the Sobel operators and detection of circles using the Hough transform. The Hough transform assists in the detection of the center and radius of a circle that approximates the margin of the ONH. Forty images of the retina from the Digital Retinal Images for Vessel Extraction (DRIVE) dataset were used to test the performance of the proposed method. The center and boundary of the ONH were independently marked by an ophthalmologist for evaluation. Free-response receiver operating characteristics (FROC) analysis as well as measures of distance and overlap were used to evaluate the performance of the proposed method. The centers of the ONH were detected with an average distance of 0.36 mm to the corresponding centers marked by the ophthalmologist; the detected circles had an average overlap of 0.73 with the boundaries of the ONH drawn by the ophthalmologist. FROC analysis indicated a sensitivity of detection of 92.5% at 8.9 false-positives per image. With an intensity-based criterion for the selection of the circle and a limit of 40 pixels (0.8 mm) on the distance between the center of the detected circle and the manually identified center of the ONH, a successful detection rate of 90% was obtained with the DRIVE dataset.

Detection of the optic nerve head in fundus images of the retina with Gabor filters and phase portrait analysis

We propose a method using Gabor filters and phase portraits to automatically locate the optic nerve head (ONH) in fundus images of the retina. Because the center of the ONH is at or near the focal point of convergence of the retinal vessels, the method includes detection of the vessels using Gabor filters, detection of peaks in the node map obtained via phase portrait analysis, and an intensity-based condition. The method was tested on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database and 81 images from the Structured Analysis of the Retina (STARE) database. An ophthalmologist independently marked the center of the ONH for evaluation of the results. The evaluation of the results includes free-response receiver operating characteristics (FROC) and a measure of distance between the manually marked and detected centers. With the DRIVE database, the centers of the ONH were detected with an average distance of 0.36 mm (18 pixels) to the corresponding centers marked by the ophthalmologist. FROC analysis indicated a sensitivity of 100% at 2.7 false positives per image. With the STARE database, FROC analysis indicated a sensitivity of 88.9% at 4.6 false positives per image.

Detection of the optic disc in images of the retina using the Hough transform

We propose a method to locate automatically the optic disc (OD) in fundus images of the retina. Based on the properties of the OD, our proposed method includes edge detection using the Sobel or the Canny method, and detection of circles using the Hough transform. The Hough transform assists in the detection of the center and radius of a circle that approximates the margin of the OD. Based on the feature that the OD is one of the bright areas in a fundus image, potential circles detected by the Hough transform are analyzed using intensity. Forty images of the retina from the DRIVE database were used to evaluate the performance of the proposed method. The success rates including both good and acceptable detections were 92.50% using the Sobel operators and 80% using the Canny edge detector.