Automated feature extraction for early detection of diabetic retinopathy in fundus images

Ravishankar, Saiprasad; Jain, Arpit; Mittal, Anurag

doi:10.1109/cvpr.2009.5206763

Cited by 166 publications

(61 citation statements)

References 9 publications

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“…Ahmad et al [8] reviewed the latest techniques in digital image processing and pattern classification employed for the detection of DR and compared them. Ravishankar et al [9] proposed a robust approach for the localization of the different features and lesions in a fundus retinal image and showed that many of the features such as the blood vessels, exudates and microaneurysms, and hemorrhages can be identified a quite scrupulous using different morphological-based operations applied in appropriate manner.…”

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Image Classification Using Deep Neural Network

2017

Asian J Pharm Clin Res

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Healthcare is an important field where image classification has an excellent value. An alarming healthcare problem recognized by the WHO that the world suffers is diabetic retinopathy (DR). DR is a global epidemic which leads to the vision loss. Diagnosing the disease using fundus images is a timeconsuming task and needs experience clinicians to detect the small changes. Here, we are proposing an approach to diagnose the DR and its severity levels from fundus images using convolutional neural network algorithm (CNN). Using CNN, we are developing a training model which identifies the features through iterations. Later, this training model will classify the retina images of patients according to the severity levels. In healthcare field, efficiency and accuracy is important, so using deep learning algorithms for image classification can address these problems efficiently.

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Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Image Classification Using Deep Neural Network

2017

Asian J Pharm Clin Res

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“…Exudate Detection [19] Exudates are bright lesions in DR image. It has sharp edges and high contrast.…”

Section: Candidate Extractionmentioning

confidence: 99%

“…Blood Vessel Extraction [19] The blood vessel ext raction is done by a morphological operations. First a closing operation is done using two different sizes of structuring element on the green channel image.…”

Section: Candidate Extractionmentioning

confidence: 99%

Comparison of Different Supervisied Classifiers in Detection of Microaneurysms

Jyoti¹

2015

IJRITCC

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Abstract-Diabetes is a rapidly increasing illness around the world. It can further cause diabetic rethinopathy(DR). If not treated properly it can make a person blind. Therefore a early detection system for (DR) is required which can be done by detecting abnormalities in eye known as microaneurysms. The main objective of this paper is to find out how different supervised classifiers responds to our morphological operation algorith m of detection of microaneurysms. The performances of the classifiers are examined by the images obtained from database DIARETDB1 wh ich also gives ground truths. (Abstract)

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“…The frequency f may be normalized by the Nyquist frequency to be between 0 and 1. The bispectrum, given by Equation (6), is a complex-valued function of two frequencies. The bispectrum which is the product of three Fourier coefficients exhibits symmetry and need only be computed in a non-redundant region.…”

Section: ) High Order Spectra Analysis (Hos) For Image Processingmentioning

confidence: 99%

“…Fuzzy C-means clustering was incorporated with spatial neighbourhood information [4]. Mathematical morphologic methods have been used [5,6]. Neural Networks and Support-vector machines were used in [7].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

Lee¹,

Zee²,

Li³

2013

JBiSE

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Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.

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Automated feature extraction for early detection of diabetic retinopathy in fundus images

Cited by 166 publications

References 9 publications

Diabetic Retinopathy Image Classification Using Deep Neural Network

Diabetic Retinopathy Image Classification Using Deep Neural Network

Comparison of Different Supervisied Classifiers in Detection of Microaneurysms

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

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