Abstract
Accurate optic disc (OD) detection is an essential yet vital step for retinal disease diagnosis. In the paper, an approach for segmenting OD boundary without manpower named full-automatic double boundary extraction is designed. There are two main advantages in it. (1) Since the performances and the computational cost produced by iterations of contour evolution of active contour models- (ACM-) based approaches greatly depend on the initialization, this paper proposes an effective and adaptive initial level set contour extraction approach using saliency detection and threshold techniques. (2) In order to handle unreliable information generated by intensity in abnormal retinal images caused by diseases, a modified LIF approach is presented by incorporating the shape prior information into LIF. We test the effectiveness of the proposed approach on a publicly available DIARETDB0 database. Experimental results demonstrate that our approach outperforms well-known approaches in terms of the average overlapping ratio and accuracy rate.
Highlights
IntroductionAccurate Optic disc (OD) localization and segmentation play an important role in retinal image analysis and eye diseases diagnosis
Many scholars have been proposing a series of approaches to improve the precision of Optic disc (OD) boundary extraction. ese approaches can be divided into four categories including classification-based [5,6,7,8,9], template-based matching [10,11,12,13,14,15,16,17], morphology-based [18,19,20], active contour models- (ACM-) based approaches [15, 21,22,23,24]
The public Standard Diabetic Retinopathy Database “Calibration Level 0” (DIARETDB0) [29] and the public dataset of retinal images namely DRISHTI-GS [30] are applied to verify the availability of our method. e DIARETDB0 and DRISHTI-GS are available and can be downloaded from the web pages http://www.it.lut.fi/project/ imageret/diaretdb0/ and http://cvit.iiit.ac.in/projects/mip/ drishti-gs/mip-dataset2/Home.php._ e_DIARETDB0 database is made up of 130 RGB color fundus images of which 20 are normal and 110 are abnormal with the fixed 1500 × 1152 resolution and 50° field of view. e ground truth is collected from two ophthalmologists. e final ground truth is acquired by averaging boundary results extracted from two ophthalmologists. e DRISHTI-GS dataset totally has 101 images of which 31 are normal and 70 are abnormal
Summary
Accurate OD localization and segmentation play an important role in retinal image analysis and eye diseases diagnosis. The localization of the OD is a crucial step for fovea detection, vessel tracking, measurement, and automated diabetic retinopathy (DR) screening [2]. The segmentation of the OD can be used for diagnosing other diseases including glaucoma, papilledema, hypertensive retinopathy, and neovascularization of the disc (NVD) [3, 4]. In many real applications, there are some challenging problems for OD segmentation due to the complex OD appearance caused by some anomalies, such as myelinated nerve fibers, peripapillary atrophy (PPA), blood vessels covered, and poor image quality. Many scholars have been proposing a series of approaches to improve the precision of OD boundary extraction. ese approaches can be divided into four categories including classification-based [5,6,7,8,9], template-based matching [10,11,12,13,14,15,16,17], morphology-based [18,19,20], active contour models- (ACM-) based approaches [15, 21,22,23,24]
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