Optic disc detection using geometric properties and GVF snake

Chickerur, Satyadhyan; Pujari, Jagadeesh; Hiremath, P. S.

doi:10.1109/icisim.2017.8122164

Cited by 5 publications

(9 citation statements)

References 15 publications

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“…Furthermore, some retinal pathology such as neovascularization (DR), wet AMD, leads to provide mistaken vessel characteristics. The work described in [1,4,5,9] have proposed usingOD features such as brightness, shape and size. The work [10,6] uses vascularization information and is based on the fact that the vessels emerge from the OD.…”

Section: Methods Classification In Terms Of Od Criteriamentioning

confidence: 99%

“…Summary of OD detection criteria.  DongboZhang et al [6]  Ivo Soares et al [7]   Xiong et Li [8]     S.Giraddi et al [9]    a. OD size Vs retina size :…”

Section: Computational Complexity Of Od Detection Methodsmentioning

confidence: 99%

“…Xiong and Li [8] have proposed an a method for locating the OD center by extracting a variety of features including vertical and horizontal vessel intensityand the size of the bright object. In the work of Giraddi et al [9], a thresholdingis employed to eliminate false positive brightness shape. The OD segmentation is then performed using the vector field gradient (GVF snake).…”

Section: Methodsof Od Detectionmentioning

confidence: 99%

“…Soares et al [7] note that, for a retinal image resolution equal to (700 × 600), the OD has a diameter equal to 80 pixels. In the work proposed in [9], the diameter is in the range of 80-100 pixels for resized image resolution at (575*750) pixels.The ratio of retina diameter and OD diameter is approximately equal to and respectively for the works relatedin [7] and [9], respectively. Thus, the diameter of OD can be approximated after the segmentation of FOV with:…”

Section: Names Of Authorsmentioning

confidence: 99%

“…It can be seen in Table 3 that all preprocessing have complexity between n² and 28.n². We deduce that preprocessing complexity is quadratic with order of O (n²).Themethods, whose location is based on the OD characteristics, proceed to detect all shapes with respect to the brightest and roundness using standard processing such as intensity threshold [9], Difference of Gaussian (DOG) filter [4] and principal component analysis [5]. These processing have quadratic processing complexity, of the order of O (n²) such as the worksdescribed in [1, 4, and 5].…”

Section: Complexity Synthesismentioning

confidence: 99%

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Computational efficiency of optic disk detection on fundus image: a survey

Sayadia

Elloumi

Akil

et al. 2018

Real-Time Image and Video Processing 2018

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Fundus image processing is getting widely used in retinopathy detection. Detection approaches always proceed to identify the retinal components, where optic disk is one of the principal ones. It is characterized by: a higher brightness compared to the eye fundus, a circular shape and convergence of blood vessels on it. As a consequence, different approaches for optic disk detection have been proposed. To ensure a higher performing detection, those approaches varied in terms of characteristics set chosen to detect the optic disk. Even the performances are slightly different, we distinguish a significant gap on the computational complexity and hence on the execution time. This paper focuses on the survey of the approaches for optic disk detection. To identify an efficient approach, it is relevant to explore the chosen characteristics and the proposed processing to locate the optic disk. For this purpose, we analyze the computational complexity of each detection approach. Then, we propose a classification approach in terms of computational efficiency. In this comparison study, we distinguish a relation between computational complexity and the characteristic set for OD detection.

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Section: Methods Classification In Terms Of Od Criteriamentioning

confidence: 99%

“…Summary of OD detection criteria.  DongboZhang et al [6]  Ivo Soares et al [7]   Xiong et Li [8]     S.Giraddi et al [9]    a. OD size Vs retina size :…”

Section: Computational Complexity Of Od Detection Methodsmentioning

confidence: 99%

Section: Methodsof Od Detectionmentioning

confidence: 99%

Section: Names Of Authorsmentioning

confidence: 99%

Section: Complexity Synthesismentioning

confidence: 99%

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Computational efficiency of optic disk detection on fundus image: a survey

Sayadia

Elloumi

Akil

et al. 2018

Real-Time Image and Video Processing 2018

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Distributed Deep Learning with Data Parallelism for Diabetic Retinopathy Classification

Patil,

Chickerur

et al. 2023

Lecture Notes in Networks and Systems

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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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Optic disc detection using geometric properties and GVF snake

Cited by 5 publications

References 15 publications

Computational efficiency of optic disk detection on fundus image: a survey

Computational efficiency of optic disk detection on fundus image: a survey

Distributed Deep Learning with Data Parallelism for Diabetic Retinopathy Classification

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

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