Detection of Non-Proliferative Diabetic Retinopathy in fundus images of the human retina

Silvia, Rosa; Vijayalakshmi, R.

doi:10.1109/icices.2013.6508242

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…Symptoms of DR included blurring of vision, difficulty reading, a sudden loss of vision in one eye, seeing rings around lights and dark spots or flashing lights [15][16].…”

Section: Diabetes and Diabetic Retinopathy (Dr)mentioning

confidence: 99%

“…NPDR is the early stage of DR and if the patient blood sugar is uncontrolled, it will rise to PDR. NPDR is characterized by the appearance of deposits of cholesterol (exudates) in the retina can be later be developed into bleeding (hemorraghe) [13][14][15]17]. However, early diagnosis and treatment from NPDR can prevent PDR from damaging the patient's vision [13].…”

Section: Diabetes and Diabetic Retinopathy (Dr)mentioning

confidence: 99%

See 1 more Smart Citation

Classification of visualization exudates fundus images results using support vector machine

Hassan¹,

Tahir²,

Zabidi³

et al. 2018

J. Fundam and Appl Sci.

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This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes.images (81 normal and 68 exudates) from MESSIDOR databas the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using SVM2 to determine its accuracy. pixels in fundus image using linear kernel function of SVM1 to diagnose DR. an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM);

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“…Symptoms of DR included blurring of vision, difficulty reading, a sudden loss of vision in one eye, seeing rings around lights and dark spots or flashing lights [15][16].…”

Section: Diabetes and Diabetic Retinopathy (Dr)mentioning

confidence: 99%

Section: Diabetes and Diabetic Retinopathy (Dr)mentioning

confidence: 99%

Classification of visualization exudates fundus images results using support vector machine

Hassan¹,

Tahir²,

Zabidi³

et al. 2018

J. Fundam and Appl Sci.

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show abstract

“…In [37], the authors describe a methodology that utilizes median filters, to extract blood vessels. Several other approaches such as [20], [38] propose to remove blood vessels by extracting the green component and applying a high pass filter for contrast enhancement. Morphological closing with a large structuring element is performed for the complete vessel removal.…”

Section: International Journal Of Engineering Research and Technology (mentioning

confidence: 99%

“…The algorithm complexity is not discussed. [38] proposes to use K-means clustering method for segmentation. In most cases of data analysis, mean and covariance would not be sufficient to extract information from a huge set of data.…”

Section: International Journal Of Engineering Research and Technology (mentioning

confidence: 99%

Extraction of Exudates from the Fundus Images A Review

Satyananda¹,

Narayanaswamy²,

Karibasappa³

2016

IJERT

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The dawn of the 20th century has heralded the entry of technology into almost every sphere of our daily lives, specially healthcare. This paper deals with review of the various methods used to detect exudates present in the eye which is the indicator of diabetic retinopathy, a dreadful eye disease. The aim here is to go through the algorithms using image processing technique giving importance to highest possible accuracy. The paper concludes with an insight into the future scope of this research and how an economically viable solution can be further obtained.

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“…Non-Proliferative Diabetic Retinopathy: NPDR which is also called background retinopathy (BDR). In BDR the blood vessels in the retina become weakened causing tiny areas of blood protruding from veins or arteries called micro aneurysms (3) . These are small blood clots occur due to capillary burst.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of New vessels for Classification of Proliferative Diabetic Retinopathy

Haq

Akram²,

Saijo³

2014

Proceedings of the 2nd International Conference on Intelligent Systems and Image Processing 2014

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Optic disc is an important anatomical feature of the retinal image and helps in the diagnosis of eye diseases which appears as a bright circular or elliptic shape in fundus image. Accurate detection of the position of optic disc is useful for the diagnosis of new vessels on optic disc. Proliferative diabetic retinopathy is an advance stage of diabetic retinopathy and causes severe visual impairments. Early detection of the diabetic retinopathy is very important for the protection of patient's vision. Automated systems for screening of proliferative diabetic retinopathy should accurately detect the blood vessels to identify vascular abnormalities. In the proposed method the vascular pattern of normal and abnormal vessels is extracted using Gabor wavelet and segmenting the blood vessels using active contour method. Support vector machine is used for the classification of neovascularization. The proposed method is evaluated using publicly available retinal image databases and results show that the proposed method identifies the vascular abnormalities with high accuracy.

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Detection of Non-Proliferative Diabetic Retinopathy in fundus images of the human retina

Cited by 9 publications

References 13 publications

Classification of visualization exudates fundus images results using support vector machine

Classification of visualization exudates fundus images results using support vector machine

Extraction of Exudates from the Fundus Images A Review

Automated Detection of New vessels for Classification of Proliferative Diabetic Retinopathy

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