A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

Haleem, Muhammad Salman; Han, Liangxiu; Hemert, Jano van; Li, Baihua; Fleming, Alan; Pasquale, Louis R.; Song, Brian J.

doi:10.1007/s10916-017-0859-4

Cited by 53 publications

(23 citation statements)

References 42 publications

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“…In order to evaluate whether our proposed method is feasible and comparable to the most recent published optic cup segmentation algorithms [35–37], also 39 glaucoma images from the public database RIM-ONE were experimented. F-score and CDR acquired by our proposed method and these referenced algorithms were listed in Table 6.…”

Section: Resultsmentioning

confidence: 99%

An efficient optic cup segmentation method decreasing the influences of blood vessels

et al. 2018

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BackgroundOptic cup is an important structure in ophthalmologic diagnosis such as glaucoma. Automatic optic cup segmentation is also a key issue in computer aided diagnosis based on digital fundus image. However, current methods didn’t effectively solve the problem of edge blurring caused by blood vessels around the optic cup.MethodsIn this study, an improved Bertalmio–Sapiro–Caselles–Ballester (BSCB) model was proposed to eliminate the noising induced by blood vessel. First, morphological operations were performed to get the enhanced green channel image. Then blood vessels were extracted and filled by improved BSCB model. Finally, Local Chart-Vest model was used to segment the optic cup. A total of 94 samples which included 32 glaucoma fundus images and 62 normal fundus images were experimented.ResultsThe evaluation parameters of F-score and the boundary distance achieved by the proposed method against the results from experts were 0.7955 ± 0.0724 and 11.42 ± 3.61, respectively. Average vertical optic cup-to-disc ratio values of the normal and glaucoma samples achieved by the proposed method were 0.4369 ± 0.1193 and 0.7156 ± 0.0698, which were also close to those by experts. In addition, 39 glaucoma images from the public dataset RIM-ONE were also used for methodology evaluation.ConclusionsThe results showed that our proposed method could overcome the influence of blood vessels in some degree and was competitive to other current optic cup segmentation algorithms. This novel methodology will be expected to use in clinic in the field of glaucoma early detection.

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Section: Resultsmentioning

confidence: 99%

An efficient optic cup segmentation method decreasing the influences of blood vessels

et al. 2018

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“…Researchers have proposed a great number of methods for the segmentation of OD and OC. Generally, these methods fall into two broad categories: nonmodel-based methods and model-based methods [11]. e former ones mainly rely on image intensity, color, and other characteristics to segment the OD and OC, including the thresholding method, the pixel classification method, and the morphological processing method.…”

Section: Introductionmentioning

confidence: 99%

“…ere are a large number of nonmodel algorithms proposed for the segmentation of OD and OC [11,13]. In most cases, the boundary of the OD and OC are approximated as circular or elliptical shape.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method

Wang

Chi

et al. 2019

Mathematical Problems in Engineering

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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. e 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 constraintbased two-layer level set method for OD and OC segmentation in fundus images. is 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.

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“…Zahoor et al conducted a study of optical disc space ROI taking with Polar Transform method [25]. Haleem et al conducted a study of optical disc space ROI taking with Region Classification Model (RCM) [26]. Karusulu conducted a research to detect optical disk area automatically using Multi-level Thresholding method [27].…”

Section: Introductionmentioning

confidence: 99%

An Automatic ROI of The Fundus Photography

Na’am¹,

Harlan²,

Putra³

et al. 2018

IJECE

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The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as darkbackground, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.

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A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

Cited by 53 publications

References 42 publications

An efficient optic cup segmentation method decreasing the influences of blood vessels

An efficient optic cup segmentation method decreasing the influences of blood vessels

Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method

An Automatic ROI of The Fundus Photography

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