Review on Fundus Image Acquisition Techniques with Data base Reference to Retinal Abnormalities in Diabetic Retinopathy

@article{KadeMahesh2013ReviewOF,
  title={Review on Fundus Image Acquisition Techniques with Data base Reference to Retinal Abnormalities in Diabetic Retinopathy},
  author={K KadeMahesh and S KashidNilesh},
  journal={International Journal of Computer Applications},
  year={2013},
  volume={68},
  pages={17-27}
}
The purpose of this paper is to Provide the information regarding Diabetic retinopathy, its imaging methods and data base in systematic manner . In this paper we Introduce the terms related to Diabetic Retinopathy along with the characteristic and features of appearance in the images .In this paper we also discuss the various image acquisition techniques of retina from fundus photography to 3D OCT Imaging. lastly we also provide the list of the current data base available with the ground truth… 

Tables from this paper

Review on Fundus Image Acquisition Techniques with Data base Reference to Retinal Abnormalities in Diabetic Retinopathy
TLDR
The terms related to Diabetic Retinopathy along with the characteristic and features of appearance in the images are introduced and the various image acquisition techniques of retina from fundus photography to 3D OCT Imaging are discussed.
A Review on the Extraction of Quantitative Retinal Microvascular Image Feature
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Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey
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Discrimination of exudates and non exudates pixels in fundus images and classification of color autocorrelogram features using multilayer perceptron and support vector machine
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Experimental results on the MESSIDOR dataset suggest that the fundus image method has the potential to be used for early indication of Diabetic Retinopathy.
CLASSIFICATION OF VISUALIZATION EXUDATES FUNDUS IMAGES RESULTS USING SUPPORT VECTOR MACHINE
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It has proven to discriminated exudates and nonExudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.
Diagnosis of Diverse Retinal Disorders Using a Multi-Label Computer-Aided System
Fundus camera or fluorescein angiography (FA) provides information about the back structure of the human eye. It has a significant role in documenting the feedback and conditions of most retinal
Novel methods in retinal vessel calibre feature extraction for systemic disease assessment
Retina and its vascular network have unique branching characteristics morphology of which will change as a result of some systemic diseases, including hypertension, stroke and diabetes. Therefore,

References

SHOWING 1-10 OF 72 REFERENCES
An effective approach to detect lesions in color retinal images
  • Huan Wang, W. Hsu, K. G. Goh, M. Lee
  • Medicine, Computer Science
    Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
  • 2000
TLDR
A novel approach that combines brightness adjustment procedure with statistical classification method and local-window-based verification strategy is proposed that is able to achieve 100% accuracy in terms of identifying all the retinal images with exudates while maintaining a 70%" accuracy in correctly classifying the truly normal retinal image as normal.
The role of domain knowledge in the detection of retinal hard exudates
TLDR
The role of domain knowledge is demonstrated in improving the accuracy and robustness of detection of hard exudates in retinal images and it is shown that this technique is able to achieve 100% sensitivity and 74% specificity in the detection ofhard exUDates.
ADRIS : an Automatic Diabetic Retinal Image Screening system
TLDR
This work employs a combination of innovative image processing and data mining techniques to automate the preliminary analysis and diagnosis of diabetic-related eye disease from the digitised retinal photographs, and shows that the system is able to accurately detect abnormal symptoms.
Automated screening system for diabetic retinopathy
The purpose is to develop an automatic computerized screening system to recognize automatically the main components of the retina, an important features of background diabetic retinopathy and
A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina
TLDR
In the framework of computer assisted diagnosis of diabetic retinopathy, a new algorithm for detection of exudates is presented and discussed, which has been tested on a small image data base and compared with the performance of a human grader.
Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.
AIMS: To determine if neural networks can detect diabetic features in fundus images and compare the network against an ophthalmologist screening a set of fundus images. METHODS: 147 diabetic and 32
Classification and Localisation of Diabetic-Related Eye Disease
TLDR
This work addresses the development of a method to quantitatively diagnose these random yellow patches in colour retinal images automatically with an overall diagnostic accuracy of 90.1% for identification of the exudate pathologies and 90.7% for optic disk localisation.
Automated feature extraction in color retinal images by a model based approach
TLDR
Novel methods to extract the main features in color retinal images have been developed and an approach to detect exudates by the combined region growing and edge detection is proposed.
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