Classification algorithm of retina images of diabetic patients based on exudates detection

Zeljković, V.; Bojic, Milena; Tameze, Claude; Valev, Ventzeslav

doi:10.1109/hpcsim.2012.6266907

Cited by 14 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Exudates are major cause of vision loss, with variable size, shape, and position, as shown in Figures 6-10a, in various stages of diabetic retinopathy: normal, mild, moderate, severe, and very severe. The information about the shape and location of anomalous retinal features is obtained by applying several mathematical transformations and models to the original retinal images described in [7].…”

Section: Methods For Exudates Detectionmentioning

confidence: 99%

“…The method for optic disk detection is proposed. The optic disk identification algorithm is given by the set of described steps: The information about the shape and location of anomalous retinal features is obtained by applying several mathematical transformations and models to the original retinal images described in [7].…”

Section: Methods For Optic Disk Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Exudates and optic disk detection in retinal images of diabetic patients

Zeljković

Bojic

Zhao

et al. 2014

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SUMMARYDiabetic retinopathy is the progressive pathological alterations in the retinal microvasculature that very often causes blindness. Because of its clinical significance, it will be helpful to have regular cost-effective eye screening for diabetic patients by developing algorithms to perform retinal image analysis, fundus image enhancement, and monitoring. The two cost-effective algorithms are proposed for exudates detection and optic disk extraction aimed for retinal images classification and diagnosis assistance. They represent the effort made to offer a cost-effective algorithm for optic disk identification, which will enable easier exudates extraction, exudates detection and retinal images classification aimed to assist ophthalmologists while making diagnoses. The proposed algorithms apply mathematical modeling, which enables light intensity levels emphasis, easier optic disk and exudates detection, efficient and correct classification of retinal images. The algorithm is robust to various appearance changes of retinal fundus images and shows very promising results. Fundus images are classified into those that are healthy and those affected by diabetes, based on the detected optic disk and exudates. The obtained results indicate that the proposed algorithm successfully and correctly classifies more than 98% of the observed retinal images because of the changes in the appearance of retinal fundus images typically encountered in clinical environments.

show abstract

Section: Methods For Exudates Detectionmentioning

confidence: 99%

Section: Methods For Optic Disk Identificationmentioning

confidence: 99%

Exudates and optic disk detection in retinal images of diabetic patients

Zeljković

Bojic

Zhao

et al. 2014

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Overall other methods [12][13] [14], to identifying the microaneurysm, Exudates, vessels segmentation for maximizing the accuracy rate is the key objective. Also, increases the complexity by added more preprocessing stages such as deblurring algorithm prior to detection, segmentation of blood vessels, rotating cross section,mathematical modeling of enhancing light intensity, morphological reconstruction.…”

Section: Related Workmentioning

confidence: 99%

Classifying Diabetic Retinopathy using Deep Learning Architecture

Chandrakumar¹,

Kathirvel²

2016

IJERT

View full text Add to dashboard Cite

A recent development in the state-of-art technology machine learning plays a vital role in the image processing applications such as biomedical, satellite image processing, Artificial Intelligence such as object identification and recognition and so on. In Global, diabetic retinopathy suffered patients growing vastly. And the fact is earliest stage could not diagnoses to normal eye vision. Increasing necessity of finding a diabetic retinopathy as earliest would stops vision loss for prolonged diabetes patient although suffered youngs'. Severity of the diabetic retinopathy disease is based on a presence of microaneurysms, exudates, neovascularization, Haemorrhages. Experts are categorized those diabetic retinopathy in to five stages such as normal, mild, moderate, severe Nonproliferative(NPDR) or Proliferative diabetic retinopathy patient(PDR). A proposed deep learning approach such as Deep Convolutional Neural Network(DCNN) gives high accuracy in classification of these diseases through spatial analysis. A DCNN is more complex architecture inferred more from human visual perspects. Amongst other supervised algorithms involved, proposed solution is to find a better and optimized way to classifying the fundus image with little pre-processing techniques. Our proposed architecture deployed with dropout layer techniques yields around 94-96 percent accuracy. Also, it tested with popular databases such as STARE, DRIVE, kaggle fundus images datasets are available publicly.

show abstract

“…Several studies use the features of hard exudates for the classification models. One study utilize the similarity measure with the mean and median values as features [7]. There are different methods for exudate segmentation, those are Fuzzy C-Means Clustering with spatial correlation [8], the adaptive region growing method [9], and the morphology approach [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Non-Proliferative Diabetic Retinopathy Based on Segmented Exudates using K-Means Clustering

Tjandrasa

Arieshanti²,

Anggoro³

2014

IJIGSP

View full text Add to dashboard Cite

Diabetic retinopathy is a severe complication retinal disease caused by advanced diabetes mellitus. Long suffering of this disease without threatment may cause blindness. Therefore, early detection of diabetic retinopathy is very important to prevent to become proliferative. One indication that a patient has diabetic retinopathy is the existence of hard exudates besides other indications such as microaneurysms and hemorrhages. In this study, the existence of hard exudates is applied to classify the moderate and severe grading of non-proliferative diabetic retinopathy in retinal fundus images. The hard exudates are segmented using K-means clustering. The segmented regions are extracted to obtain a feature vector which consists of the areas, the perimeters, the number of centroids and its standard deviation. Using three different classifiers, i.e. soft margin Support Vector Machine, Multilayer Perceptron, and Radial Basis Function Network, we achieve the accuracy of 89.29%, 91.07%, and 85.71% respectively, for 56 training data and 56 testing data of retinal images.

show abstract

Classification algorithm of retina images of diabetic patients based on exudates detection

Cited by 14 publications

References 13 publications

Exudates and optic disk detection in retinal images of diabetic patients

Exudates and optic disk detection in retinal images of diabetic patients

Classifying Diabetic Retinopathy using Deep Learning Architecture

Classification of Non-Proliferative Diabetic Retinopathy Based on Segmented Exudates using K-Means Clustering

Contact Info

Product

Resources

About