Gabor Wavelet Based Vessel Segmentation in Retinal Images M. Usman Akram * , Anam Tariq § , Sarwat Nasir † and Shoab A. Khan ‡ Computer Engineering * , Software Engineering § , Computer Sciences and Engineering † , Electrical Engineering ‡ College of Electrical and Mechanical Engineering *, ‡ National University of Sciences & Technology *, ‡ Fatima Jinnah Women University § , Bahria University † , Pakistan. Email: usmakram@gmail.com * , anam.tariq86@gmail.com § , sarwatnasir83@gmail.com † , shoab@carepvtltd.com ‡ Abstract— Retinal image vessel segmentation and their branching pattern are used for automated screening and diag- nosis of diabetic retinopathy. Vascular pattern is normally not visible in retinal images. We present a method that uses 2-D Gabor wavelet and sharpening filter to enhance and sharpen the vascular pattern respectively. Our technique extracts the vessels from sharpened retinal image using edge detection algorithm and applies morphological operation for their refinement. This technique is tested on publicly available DRIVE database of manually labeled images. The validation of our retinal image vessel segmentation technique is supported by experimental results. I. I NTRODUCTION D IABETES affects almost every one out of ten people, and has associated complications such as vision loss, heart failure and stroke [1]. Diabetic eye disease refers to a group of eye problems that people with diabetes may face as a complication. Patients with diabetes are more likely to develop eye problems such as cataracts and glaucoma, but the disease’s affect on the retina is the main threat to vision [2]. Complication of diabetes, causing abnormalities in the retina and in the worst case blindness or severe vision loss, is called Diabetic Retinopathy [2]. Diabetic retinopathy is the result of microvascular retinal changes. In some people with diabetic retinopathy, blood vessels may swell and leak fluid. In other, new abnormal blood vessels grow on the surface of the retina [3]. The retina is the light-sensitive tissue at the back of the eye. A healthy retina is necessary for good vision [4]. Retinal vascular pattern facilitates the physicians for the purposes of diagnosing eye diseases, patient screening, and clinical study [4]. Inspection of blood vessels provides the information regarding pathological changes caused by oc- ular diseases including diabetes, hypertension, stroke and arteriosclerosis [5]. The hand mapping of retinal vasculature is a time consuming process that entails training and skill. Automated segmentation provides consistency and reduces the time required by a physician or a skilled technician for manual labeling [2]. Retinal vascular pattern is used for automatic generation of retinal maps for the treatment of age-related macular degeneration [6], extraction of characteristic points of the retinal vasculature for temporal or multimodal image regis- tration [7], retinal image mosaic synthesis, identification of the optic disc position [8], and localization of the fovea [9]. The challenges faced in automated vessel detection include wide range of vessel widths, low contrast with respect with background and appearance of variety of structures in the image including the optic disc, the retinal boundary and other pathologies [10]. Methods based on vessel tracking to obtain the vascula- ture structure, along with vessel diameters and branching points have been proposed by [11]-[16]. Tracking consists of following vessel center lines guided by local information. In [22], ridge detection was used to form line elements and partition the image into patches belonging to each line element. Pixel features were then generated based on this representation. Many features were presented and a feature selection scheme is used to select those which provide the best class separability. Papers [17]-[20] used deformable models for vessels segmentation . Chuadhuri et al. [21] proposed a technique using matched filters to emphasize blood vessels. An improved region based threshold probing of the matched filter response technique was used by Hoover et al. [23]. In this paper, we present the colored retinal image vessel segmentation technique that enhances and sharpens the vas- cular pattern using 2-D Gabor wavelet and sharpening filters. Our techniques creates a binary mask for vessel segmentation applying edge detection algorithm on sharpened retinal image and a fine segmentation masks is obtained by applying morphological dilation operation. The paper is organized in four sections. In Section II, a schematic overview of our implementation methodology is illustrated. Section II also presents the step by step techniques required for colored retinal image vessels segmentation. Experimental results of the tests on the images of the DRIVE database and their analysis are given in Section III followed by conclusion in Section IV. II. BLOOD VESSEL SEGMENTATION The monochromatic RGB retinal image is taken as an input and 2-D Gabor wavelet is used to enhance the vas- cular pattern especially the thin and less visible vessels 978-1-4244-2760-4/09/$25.00 ©2009 IEEE Authorized licensed use limited to: Bahria University. Downloaded on August 24, 2009 at 00:21 from IEEE Xplore. Restrictions apply.