A Novel Integrated Approach Using Dynamic Thresholding and Edge Detection (IDTED) for Automatic Detection of Exudates in Digital Fundus Retinal Images

Sagar, Anantha Vidya; Balasubramaniam, Subramaniam; Chandrasekaran, V.

doi:10.1109/iccta.2007.16

Cited by 19 publications

(6 citation statements)

References 7 publications

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“…Success of the thresholding mechanism depends on the value of thresholding parameter (T). For the proposed method we have used dynamic thresholding technique [16], [2].The image is divided into blocks of 64x64 pixels each such that atleast 50% overlap between adjacentpairs is obtained. Histogram of each of these blocks is then computed.…”

Section: Dynamic Thresholdingmentioning

confidence: 99%

A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

Banerjee

Kayal

2014

IJCT

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Diabetic retinopathy is considered to be one of the major causes of blindness among diabetes mellitus patients. Due to diabetic retinopathy blood vessels of retina gets damaged and fat, lipoprotein substances gets leaked out of the damaged blood vessels and are deposited in the intra retinal space. These substances are viewed as yellowish or whitish in color and are termed as exudates. They are the most important visible sign of the presence of diabetic retinopathy. Exudates are of two types, (a) hard exudates and (b) soft exudates. If the disease is not detected in early stages then it may lead to complete loss of vision to the diabetes patients. Detection of exudates is extremely difficult to detect by visual inspection due to small inner diameter of retina and inadequate lighting conditions. An efficient image analysis program can detect the presence effectively. In this paper we have proposed an automatic method for detection of hard exudates. The proposed method exhibits a sensitivity of 97.60% and specificity of 93% and accuracy of 95.70%.

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Section: Dynamic Thresholdingmentioning

confidence: 99%

A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

Banerjee

Kayal

2014

IJCT

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“…A feature based method has used detection of contours of high intensity areas and regions with yellow colour and high contrast which is then dilated to obtain the marker image and the exudates are finally segmented using selective morphological reconstruction [6]. A method based on dynamic thresholding of images after histogram specification and local contrast enhancement has been shown to have sensitivity and specificity of 99% and 93% respectively on a set of 25 images [7]. Statistical approaches based on mixture models have also been used for thresholding hard exudates from the background [8].…”

Section: Introductionmentioning

confidence: 99%

Quantitative clinical marker extraction from colour fundus images for non-proliferative Diabetic Retinopathy grading

Poddar

Jha

Chakraborty

2011

2011 International Conference on Image Information Processing

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This paper proposes robust methods for segmentation of clinically significant features of fundus images, from the point of view of detection and gradation of Diabetic Retinopathy as well as Maculopathy. After pre-processing to remove intra-and inter-image illumination variances, the optic disk and fovea are detected and exudates, haemorrhages and microaneurysms are segmented out using modified morphological techniques. The retina is then divided into circular zones concentric around fovea and the pattern and extent of abnormalities in these zones is used to classify the abnormal images into different grades of nonproliferative diabetic retinopathy (NPDR) and maculopathy.

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“…The shortcomings of their proposed algorithm is its extensive operations and its possible failure to identify small exudates which are present in many DR cases. The authors in [7] presented a new method for exudates detection with preprocessing techniques such as histogram specification and local contrast enhancement are integrated with dynamic thresholding and edge detection. Researchers have also used neural networks to locate exudate [2], [5], [9].…”

Section: Introductionmentioning

confidence: 99%

An information geometry based scheme for hard exudate detection in fundus Images

Mishra

Singh

Chawla

2011

2011 Annual IEEE India Conference

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Our research focuses on the detection of hard exudates (HEs) in fundus Images for identification of the condition of Diabetic Retinopathy (DR). HEs have been found to be the most specific markers for the presence of retinal oedema, and are also one of the most prevalent lesions during early stages of DR. In order to detect HEs, we have introduced a novel way of using the Distance Learning Metric using a Non-linear Kernels function. We have also introduced a new method to remove the OD in the post-processing stage based on variance calculation. In this the contention in the worst case is resolved by the use of the more robust vertical blood vessel edge detection method. Overall, with the combined implementation of the standard algorithm and the novel algorithm proposed by us, we have been able to achieve classification results of 96.67% (which is better than the results contended in the present literature surveyed by us) with very less computational cost of the algorithm as compared to the computational costs posed by conventional algorithms.

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A Novel Integrated Approach Using Dynamic Thresholding and Edge Detection (IDTED) for Automatic Detection of Exudates in Digital Fundus Retinal Images

Cited by 19 publications

References 7 publications

A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

Quantitative clinical marker extraction from colour fundus images for non-proliferative Diabetic Retinopathy grading

An information geometry based scheme for hard exudate detection in fundus Images

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