Segmentation of Fundus Eye Images Using Methods of Mathematical Morphology for Glaucoma Diagnosis

Stąpor, Katarzyna; Świtoński, Adam; Chrástek, Radim; Michelson, Georg

doi:10.1007/978-3-540-25944-2_6

Cited by 38 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Their method depends on the correctness of blood vessels extraction. Stapor et al [13] utilized mathematical morphology to detect the optic disc and its boundary by extraction based on geodesic reconstruction by dilation. Lowell et al [14] described a method based on a specialized correlation filter to detect approximately the center of the optic disc.…”

Section: Existing Methodsmentioning

confidence: 99%

Optic disc detection and boundary extraction in retinal images

Basit¹,

Fraz²

2015

Appl. Opt.

View full text Add to dashboard Cite

With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.

show abstract

Section: Existing Methodsmentioning

confidence: 99%

Optic disc detection and boundary extraction in retinal images

Basit¹,

Fraz²

2015

Appl. Opt.

View full text Add to dashboard Cite

show abstract

“…Unsupervised approaches provide OC and OD segmentation without any learning phase. Among unsupervised state-of-the-art approaches, image processing techniques such as image thresholding or morphological operators have been frequently used to segment both OC and OD areas (Aquino et al, 2010;Stapor et al, 2004). Then, CDR calculation leads to glaucoma screening, as a binary classification between healthy and glaucomatous patients is generally operated (Singh et al, 2015b).…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Detection Performance (DIARETDB0dataset) (%) Walter [30] 77.5 92.13 -Sopharak [32] 95 59.55 -Seo [33] 95 80.89 -Kande [34] 95 86.51 -Stapor [35] 87.5 78.65 -Lupascu [36] 95 88.76 -Welfer [37] 100 97.70 -Our Method 100 97.75 97.70 Table I shows the performance of the optic disc location on the DRIVE, DIARETDB0 and DIARETDB1 datasets. The performance of our method is compared with the alternative methods: Walter et al [30], Sopharak et al [32], Seo et al [33], Kande et al [34], Stapor et al [35], Lupascu et al [36] and Welfer et al [37] taken from [37]. The comparison indicates that the proposed method achieves the best performance in detecting the optic disc than alternative methods.…”

Section: B Performance Measuresmentioning

confidence: 99%

Level Set Segmentation of Optic Discs from Retinal Images

Wang¹,

Kaba²,

Li³

2015

JOMB

View full text Add to dashboard Cite

Analysis of retinal images can provide important information for detecting and tracing retinal and vascular diseases. The purpose of this work is to design a method that can automatically segment the optic disc in the digital fundus images. The template matching method is used to approximately locate the optic disc centre, and the blood vessel is extracted to reset the centre. This is followed by applying the Level Set Method, which incorporates edge term, distance-regularization term and shape-prior term, to segment the shape of the optic disc. Seven measures are used to evaluate the performance of the methods. The effectiveness of the proposed method is evaluated against alternative methods on three public data sets DRIVE, DIARETDB1 and DIARETDB0. The results show that our method outperforms the state-of-the-art methods on these datasets. 

show abstract

Segmentation of Fundus Eye Images Using Methods of Mathematical Morphology for Glaucoma Diagnosis

Cited by 38 publications

References 9 publications

Optic disc detection and boundary extraction in retinal images

Optic disc detection and boundary extraction in retinal images

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

Level Set Segmentation of Optic Discs from Retinal Images

Contact Info

Product

Resources

About