Segmentation of Exudates and Optic Disk in Retinal Images

Kande, Giri Babu; Subbaiah, P. Venkata; Savithri, T. Satya

doi:10.1109/icvgip.2008.36

Cited by 52 publications

(16 citation statements)

References 6 publications

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“…The next step in our method is to detect the optic disk, and remove it from the f 5 image. Despite the fact that several methods have been proposed in the literature for detecting the optic disk (see Seo et al [19], Kande et al [20] and Lupaş cu et al [21]), we have developed our own morphological method for detecting the optic disk because of its adequacy to our present needs, but the above mentioned methods also could be adopted. Our approach to detect the optic disk relies on mathematical morphology techniques, and has two main stages, namely: (1) detection of the optic disk location; (2) detection of the optic disk boundary.…”

Section: Stage A: Coarse Detection Of Exudatesmentioning

confidence: 97%

A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

Welfer

Scharcanski

Marinho

2010

Computerized Medical Imaging and Graphics

153

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Section: Stage A: Coarse Detection Of Exudatesmentioning

confidence: 97%

A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

Welfer

Scharcanski

Marinho

2010

Computerized Medical Imaging and Graphics

153

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“…They modeled the optic disc as a circle, which may not be the case for all the ODs. Kande et al [17] approximated the maximum local variance for the detection of the optic disc and used a geometric active contour with variational formation for detection of the optic disc boundary. Different morphological methods have been applied by Sopharak et al [18] to detect the optic disc as part of an exudates detection algorithm.…”

Section: Existing Methodsmentioning

confidence: 99%

Optic disc detection and boundary extraction in retinal images

Basit¹,

Fraz²

2015

Appl. Opt.

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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.

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“…In this paper [16], Giri Babu Kande et al proposed the algorithm to confine the optic disc in ocular fundus images. Firstly, the optic centre was detected by finding the centre or region with maximum local variance.…”

Section: Segmentation Of Exudates and Optic Disc In Retinal Imagesmentioning

confidence: 99%

“…Exudates Detection: First row: Color retinal images; Second row: contrast enhanced RGB images; Third row: optic disk areas eliminated from the retinal images; Fourth row: exudate candidates after SWFCM; Fifth row: Results from third row superimposed on Intensity images[16] …”

mentioning

confidence: 99%

A Comparative Study of Retinal Image Processing Technique for Blood Vessel Segmentation

2016

IJSR

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Blood vessel assessment from retinal images is an important factor for many eye related disease. Retinal blood vessel segmentation is done for identifying ophthalmic disease like glaucoma, diabetic retinopathy, and macular degeneration. Glaucoma is a condition of elevated pressure within the eyeball, which causes gradual loss of sight. Diabetic retinopathy occurs due to damage of the tiny blood vessels that nourish the retina. It results in blood and other fluids leakage that swells retinal tissue that causes clouding of vision. Macular Degeneration happens when central portion of the retina deteriorates. Thus, blood vessel segmentation efficiently reduces the risk of blindness. The manual comparison of fundus images taken at different moments is a very time-consuming job and prone to human error because of the distortions between images that make superposition very difficult, and also there are large numbers of lesions that need to be compared. Hence, computer assisted approach is needed. We have compared different methods of retinal blood vessel segmentation based on parameters like accuracy, sensitivity, specificity and algorithms that have been used for blood vessel segmentation method.

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Segmentation of Exudates and Optic Disk in Retinal Images

Cited by 52 publications

References 6 publications

A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

Optic disc detection and boundary extraction in retinal images

A Comparative Study of Retinal Image Processing Technique for Blood Vessel Segmentation

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