Segmenting the Optic Disc in Retinal Images using Thresholding

Choukikar, Prashant; Patel, Arun Kumar; Mishra, Ravi Shankar

doi:10.5120/16385-5937

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…Here we have work with some segmentation and classification technique to segment the optic disc and classifies the image based on their feature selection of the image. In this paper, we conclude that the thresholding technique is best for segment the specific region in the retinal image [13], as well as SVM classifier is best for classifies the image based upon their category [4].These two techniques are very helpful to ophthalmologist to glaucoma assessment as efficient manner.…”

Section: Discussionmentioning

confidence: 77%

“…To improve the accurate identification, the classifier can be trained by discriminatory changes in these features with glaucoma occurrence [13], [14]. The adaptive thresholding technique which is used to segment the optic disc and cup based on the pixel intensities which uses segmentation of optic disc and cup by local features from the fundus image that making it invariant to quality of image.…”

Section: E An Adaptive Threshold Based Image Processing Techniquementioning

confidence: 99%

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Survey on Detection of Glaucoma in Fundus Image by Segmentation and Classification

Vijayasekar¹,

Dhivya²,

Dhanalakshmi³

et al. 2015

IJERT

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Reliable glaucoma detection in digital fundus images is still an open issue in biomedical image processing. The detection of glaucoma in retinal fundus image is essential for preventing from the vision loss. Glaucoma is an irretrievable chronic eye disease which leads to blindness that caused due to the damage of optic nerves. The time of glaucoma detection is very important to be slowed down by treatment whereas glaucoma cannot be cured. Particularly there is no effective method for detection of glaucoma in current status. Nowadays many studies have shown that the detection or screening of glaucoma in 2D retinal fundus image. This paper addresses the survey on various methods of segmentation and classification technique to detect the glaucoma from the retinal images based on the Cup to Disc Ratio (CDR) evaluation of preprocessed image. This survey paper presents an image processing technique for segmentation of optic disc and cup as well as diagnosis of glaucoma using obtained the features from the image based on the study of adaptive thresholding technique and SVM classification technique compared to remaining or existing algorithms.

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Section: Discussionmentioning

confidence: 77%

Section: E An Adaptive Threshold Based Image Processing Techniquementioning

confidence: 99%

Survey on Detection of Glaucoma in Fundus Image by Segmentation and Classification

Vijayasekar¹,

Dhivya²,

Dhanalakshmi³

et al. 2015

IJERT

View full text Add to dashboard Cite

show abstract

“…A slightly different approach was taken by C. Pereira, L. Gonçalves, and M. Ferreira by using the Ant colony optimization algorithm [7] to localize the optic disk. A simple and efficient approach for segmentation of the optic disc by thresholding the morphologically connected regions and boundary extraction was done in "Segmenting the Optic Disk in Retinal Images using Bi-Histogram Equalization and Thresholding the Connected Regions" [8].…”

Section: Litriture Reviewmentioning

confidence: 99%

An Improved Method for Optic Disc Localization

Karunanayake¹,

Gnanasekera²,

Kodikara³

2015

IJCA

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An accurate approach for localization and segmentation of an optic disk (OD) in the retinal images is one of the most imperative tasks in an automated screening system. The retinal fundus images analysis is extensively used in the diagnosis and treatment of several eye diseases such as glaucoma and diabetic retinopathy. This research brings out a new algorithm that has not been used before to detect and segment the optic disk in all categories of retinal images, specifically healthy retinal images plus anomalous, or in other words fundus images affected due to diseases. The technique adopted for the separation of the optic disk is centered on histogram specification, mathematical morphological operations includes erosion as well as dilation along with proper thresholding and detection of circles using the Hough Transform technique. The proposed procedure has been tested on standard databases provided for ophthalmic image processing researches on internet, Diaretdb0, Diaretdb1 and local databases collected from the National Eye Hospital and the Vision Care (Pvt) Ltd. The proposed algorithm is able to locate the OD in 90% of all tested images.

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“…It is updated daily with between 200 and 4,000 citations. Such medical citations that deal with different medical diagnosis through image processing as in [56][57][58][59][60][61].…”

Section: Medline Clusteringmentioning

confidence: 99%

MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

Karâa

Ashour

Sassi³

et al. 2015

Intelligent Systems Reference Library

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MEDLINE is the largest biomedical literature database. It is updated daily with 200-4,000 citations. This permanent growth induces the need of a good MEDLINE abstract clustering to accelerate the procedure of research and information retrieval. Several works have been developed in this context, but clustering MEDLINE abstracts are still an area where researchers are trying to propose new approaches to better clustering. Over the last few years, evolutionary algorithms have been widely applied to clustering problems because of their ability to avoid local optimal solutions and converge to a global one. In this article, a new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.

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Segmenting the Optic Disc in Retinal Images using Thresholding

Cited by 13 publications

References 13 publications

Survey on Detection of Glaucoma in Fundus Image by Segmentation and Classification

Survey on Detection of Glaucoma in Fundus Image by Segmentation and Classification

An Improved Method for Optic Disc Localization

MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

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