Abstract
Glaucoma is a group of eye conditions, which can seriously damage optic nerves because of an elevated intraocular pressure. Nowadays, glaucoma has become one of the principal causes of blindness that results in irreversible visual loss. Early screening and treatment of glaucoma can prevent further progression of optic nerve degeneration effectively. The vertical cup-to-disc ratio (CDR) is a commonly used measurement for the detection of glaucoma, and therefore accurate segmentation of optic disc (OD) and optic cup (OC) regions in retinal fundus images is of great significance. In this paper, we present a prior shape constraint-based two-layer level set method for OD and OC segmentation in fundus images. This method uses two different layers of one level set function to represent the OD and OC boundaries. In this method, the distance regularized term is designed to guarantee that the distance between the OD and OC varies smoothly. By introducing the prior shape constraint term energy, the final segmentation results of OD and OC are always in the shape of approximate ellipses. In addition, the proposed method has the property of dealing with the intensity inhomogeneity of fundus images through the local fitting energy embedded. Experiments on images from the Baidu Research database demonstrate that the proposed method has superior performance in terms of accuracy and effectiveness.
Highlights
Nowadays, eye diseases are serious diseases that damage human health. e most common eye diseases include glaucoma, diabetic retinopathy, and maculopathy [1]
In order to solve the problem of segmentation of optic disc (OD) and optic cup (OC) in fundus images, this paper proposes a distance regularized two-layer level set method based on prior elliptic shape constraint (DRLSEC)
It is obvious that the proposed DRLSEC method and DR2LS method are able to deal with intensity inhomogeneity effectively, which is due to their local intensity fitting energy
Summary
Eye diseases are serious diseases that damage human health. e most common eye diseases include glaucoma, diabetic retinopathy, and maculopathy [1]. In [17], the fuzzy c-means method which was integrated with morphological techniques was applied to the segmentation of OD and OC and provided desirable results All these operations were carried out in green channel of fundus images. In [25], Wong et al proposed an edge-based variational level set method with the ellipse fitting energy to extract the OD boundary in fundus images. In [26], Dai et al proposed a novel active contour model that consists of boundary energy, shape energy, and region energy to segment the OD region automatically in fundus images. In order to solve the problem of segmentation of OD and OC in fundus images, this paper proposes a distance regularized two-layer level set method based on prior elliptic shape constraint (DRLSEC).
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