Abstract
At the center of the macula, fovea plays an important role in computer-aided diagnosis. To locate the fovea, this paper proposes a vessel origin (VO)-based parabolic model, which takes the VO as the vertex of the parabola-like vasculature. Image processing steps are applied to accurately locate the fovea on retinal images. Firstly, morphological gradient and the circular Hough transform are used to find the optic disc. The structure of the vessel is then segmented with the line detector. Based on the characteristics of the VO, four features of VO are extracted, following the Bayesian classification procedure. Once the VO is identified, the VO-based parabolic model will locate the fovea. To find the fittest parabola and the symmetry axis of the retinal vessel, an Shift and Rotation (SR)-Hough transform that combines the Hough transform with the shift and rotation of coordinates is presented. Two public databases of retinal images, DRIVE and STARE, are used to evaluate the proposed method. The experiment results show that the average Euclidean distances between the located fovea and the fovea marked by experts in two databases are 9.8 pixels and 30.7 pixels, respectively. The results are stronger than other methods and thus provide a better macular detection for further disease discovery.
Highlights
The fovea is a small depression located in the center of the macula area of the retina [1]
Walter et al [22] and Mahfouz et al [23] only reveal the optic disc (OD) detection algorithms. Their fovea data are both obtained by the traditional parabolic model, which takes the OD center as the vertex of the parabolic-like vasculature
The results reflect the superiority of taking the vessel origin (VO) as the vertex of the parabolic model
Summary
The fovea is a small depression (about 1 mm in diameter) located in the center of the macula area of the retina [1]. Fovea detection is necessary because the grade of diabetic retinopathy depends on their distance to the fovea. Detection and diagnosis of retinopathies decrease the risk of retinal lesion and effectively control the illness. Due to problems of retinal image quality, such as poor contrast and physicians’ subjective observation, the diagnoses can be unstable and uncertain. Computer analysis systems for retinal images can offer an efficient and stable assistance for diagnosis. The performance of the computer analysis system is generally influenced by retinal anatomy detection. The fovea is difficult to observe, but it usually is where the diabetic retinopathy occurs. The consequence of this obvious visual structure in retinal images is that fovea localization becomes very difficult. A great deal of research has studied this issue, there are still many restrictions to their methods
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