Joint optic disc and cup boundary extraction from monocular fundus images

Chakravarty, Arunava; Sivaswamy, Jayanthi

doi:10.1016/j.cmpb.2017.06.004

Cited by 55 publications

(55 citation statements)

References 35 publications

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“…Although, the method was unsuccessful in translating the sampling points to obtain more reliable results. Chakravarty and Sivaswamy in [38] proposed a joint segmentation model for disc and cup boundary extraction. Textural features of the disc and cup were extracted using the coupled and sparse dictionary approaches.…”

Section: Optic Disc and Cup Segmentationmentioning

confidence: 99%

“…A summary of the aforementioned disc and cup extraction approaches is given in Table 2, illustrating the particular method, datasets used, and the number of images. [38] proposed a joint segmentation model for disc and cup boundary extraction. Textural features of the disc and cup were extracted using the coupled and sparse dictionary approaches.…”

Section: Optic Disc and Cup Segmentationmentioning

confidence: 99%

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Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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Section: Optic Disc and Cup Segmentationmentioning

confidence: 99%

Section: Optic Disc and Cup Segmentationmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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“…Then, a set of retinal images with the manual boundaries of the OC and the OD is used to train a specific classifier then detect the 4 OC and OD areas with the computed features. For example, Chakravarty and Sivaswamy (2017) proposed a boundarybased method for OC-OD segmentation, where several image features such as color gradients and depth estimations are retrieved from retinal images. A Conditional Random Field is formulated, using an energy minimization criteria to fit on the boundaries.…”

Section: Cdr-based Glaucoma Screening Related Workmentioning

confidence: 99%

Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images

Mvoulana

Kachouri

Akil

2019

Computerized Medical Imaging and Graphics

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Background and Objective: Visual impairment affects a significant part of the population worldwide. Glaucoma is one of these main causes, a chronic eye disease leading to progressive vision loss. Early glaucoma screening is an important task, allowing a slowing down of the pathology spreading and avoidance of irreversible vision damages. When manual assessment by experts suffers from disadvantages, exploiting the relevant Cup-to-Disc Ratio (CDR) feature as a structural indicator to assess the damage to the optic nerve head (ONH) is an efficient way for early glaucoma screening and diagnosis. Methods: In this paper, we propose a new fully automated methodology for glaucoma screening and diagnosis from retinal fundus images. In order to allow eye examination in remote locations with limited access to clinical facilities, we focus in this work on the development of a computationally-efficient algorithm for further implementation on mobile devices. First, the method provides a robust optic disc (OD) detection method, combining a brightness criterion and a template matching technique, to effectively detect the optic disc (OD) even in the presence of bright lesions associated to pathological cases. Second, an efficient optic cup (OC) and optic disc (OD) segmentation is performed, using a texture-based and model-based approach. Finally, Cup-to-Disc Ratio (CDR) computation leads to glaucoma screening with a classification between healthy and glaucomatous patients. Results: The proposed approach for glaucoma screening and diagnosis have been tested on the publicly available DRISHTI-GS1 dataset. Fifty retinal images are provided and labeled healthy or glaucomatous by trained specialists. The method achieves 98% of accuracy on final glaucoma screening and diagnosis, and excellent performance rates on evaluation metrics, outperforming the state-of-the-art CDR feature-based approaches. Conclusions: We proposed a fully automated method for glaucoma screening and diagnosis from retinal images. Excellent performance was obtained on final screening, classifying healthy and glaucomatous subjects. As it effectively detects the presence of glaucoma, in a low-computational manner, the approach can be part of a mobile help-diagnosis system, to improve the final diagnosis by the specialist and develop widespread visual health programs.

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“…The main segmentation techniques include template-based methods [4], [5], boundary detection [6], [7], hand-crafted visual feature approach, and deep learning segmentation methods [8], [9], [10], [11]. In these methods, the template-based method models the OD as s circular or elliptical object and use a circular Hough transforms [4], [5] or sliding band filter [6] to obtain an approximate boundary of optic disc. The method based on boundary detection needs to mark multiple landmark points [6], or requires each pixel to have a direct edge in the 15-pixel neighborhood, and does not consider depth information [7].…”

Section: Introductionmentioning

confidence: 99%

“…In these methods, the template-based method models the OD as s circular or elliptical object and use a circular Hough transforms [4], [5] or sliding band filter [6] to obtain an approximate boundary of optic disc. The method based on boundary detection needs to mark multiple landmark points [6], or requires each pixel to have a direct edge in the 15-pixel neighborhood, and does not consider depth information [7]. The hand-crafted visual feature approaches converts boundary problems into pixel classification problems, and obtains satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Joint Optic Disc and Dup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

Liu

Pan

Song

2020

Preprint

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Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio is the main indicator used to screen and diagnose glaucoma, optic disc and optic cup segmentation can assist computer in diagnosing glaucoma.Therefore, accurate segmentation of optic disc and optic cup is beneficial to the screening and diagnosis of glaucoma and helps patients diagnose and treat early. Method: In this paper, we consider the segmentation of the optic disc and the optic cup as a multi-category semantic segmentation task, and proposed a deep learning-based model model named DDSC-Net(densely connected depthwise separable convolution network) to extract the optic disc and the optic cup. The backbone network is composed of densely connected deep separable convolution blocks to form a deeper network and the image pyramid input is introduce into the input layer to widen the network. In the post-process, we apply the morphological method to refine the output segmentation results. Result: The proposed method is evaluated on two publicly available datasets, DRISHTI-GS dataset and REFUGE dataset. And the experiment results show that our DDSC-Net outperforms state-of-the-art optic disc and cup segmentation methods in terms of disc coefficients and Jaccard score. Furthermore,our method achieves the best result on the more challenging optic cup segmentation task. Conclusion: The promising segmention performances reveal that our method has potential in assisting the screening and diagnosis of the glaucoma.

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Joint optic disc and cup boundary extraction from monocular fundus images

Cited by 55 publications

References 35 publications

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images

Joint Optic Disc and Dup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

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