Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images

Kande, Giri Babu; Savithri, T. Satya; Subbaiah, P. Venkata

doi:10.1007/s10278-009-9246-0

Cited by 77 publications

(23 citation statements)

References 12 publications

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“…Localization of Optic Disc, its elimination and yellow exudates detection is done using SVM classifier in [8]. Red lesions like micro aneurysms and hemorrhages are detected in [9]. Gaussian shaped curve is used for identifying red lesions.…”

Section: Introductionmentioning

confidence: 99%

Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM

Chowdhury¹

2016

CSSP

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Diabetic Retinopathy and Age related Macular Degeneration are two major retinal diseases which are creating serious concern in today's age. Detection of preliminary signs of abnormalities due to these diseases is hard and time consuming for the ophthalmologists as the abnormal objects are very fine and small in size. As early detection of abnormalities can prevent permanent vision loss, a semi automated system is developed to detect the affected portion of retina and is tested with some retinal images. A training image set is used to train a support vector machine classifier. A test image set is given to the classifier for automatic detection of disease type. Efficiency of the classifier is tested comparing the actual value and predicted value by the classifier.

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Section: Introductionmentioning

confidence: 99%

Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM

Chowdhury¹

2016

CSSP

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“…[25] The sensitivity is calculated according to Eq. (16). (16) The specificity is defined as the ability of a test to exclude properly people without a disease or condition [25].…”

Section: Resultsmentioning

confidence: 99%

“…Badea et al [15] detect all objects of the retina with a proposed method, called Expanding Gradient Method (EGM), and then the candidate regions for red lesions are extracted. Kande et al [16] use operators of mathematical morphology to extract candidate regions, proposing a classifier based on Support Vector Machines (SVM). Jaafar et al [5], Niemeijer et al [9] and Ravishankar et al [17] also used techniques of mathematical morphology for detection of red lesions, getting satisfactory results.…”

Section: Related Workmentioning

confidence: 99%

“…As noted in [2], fundus images often suffer from nonuniform illumination, poor contrast and noise, thus these images have to go through preprocessing phases. According to [16], the biggest challenges for detection of red lesions are the segmentation of microaneurysms in areas of low contrast and the presence of white lesions in the retina (exudates). It may also be mentioned that microaneurysms and hemorrhages have various sizes and shapes, making it difficult to perform detection of all of them in the image, mainly because the normal structures of the retina, as the fovea and the blood vessels, can be confused with lesions [19].…”

Section: Related Workmentioning

confidence: 99%

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Automatic Detection of Microaneurysms and Hemorrhages in Color Eye Fundus Images

Júnior¹,

Welfer²

2013

IJCSIT

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This paper presents an approach for automatic detection of microaneurysms and hemorrhages in fundus images. These lesions are considered the earliest signs of diabetic retinopathy. The diabetic retinopathy is a disease caused by diabetes and is considered as the major cause of blindness in working age population. The proposed method is based on mathematical morphology and consists in removing components of retinal anatomy to reach the lesions. This method consists of five stages: a) pre-processing; b) enhancement of low intensity structures; c) detection of blood vessels; d) elimination of blood vessels; e) elimination of the fovea. The accuracy of the method was tested on a public database of fundus images

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“…The length filtering is applied to remove misclassified and secluded pixels. The candidate lesions are obtained by taking difference of the length filtered image and local entropy threshold image [16]. The remained isolated objects give tentative lesions for MAs.…”

Section: Candidate Region Extractionmentioning

confidence: 99%

An efficient method for early stage detection of diabetic retinopathy

Shirbahadurkar¹,

Mané²,

Jadhav³

2017

IJET

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Diabetic Retinopathy (DR) is one of the leading causes of blindness. The early detection and treatment of DR is significant to save the human vision. The presence of microaneurysms (MAs) is the first sign of the disease. The correct identification of MAs is an essential for finding of DR at the early stages. In this paper, we propose a three phase system for efficient recognition of MAs. The tentative MA lesions are recovered from the fundus image in the first stage. To accurately classify an extracted candidate region into MA or non-MA, the second stage prepares an attribute vector for each tentative MA lesion based on shape, intensity and statistical properties. The third stage is a classification step to classify as MAs and Non-MAs for early stage detection of DR. We present a holoentropy enabled decision tree classifier which combines entropy and total correlation. The best feature for decision tree is selected based on holoentropy to enhance the correctness of the classification. The implemented system is experimented using fundus image database DIARETDB1. The proposed method achieved an overall accuracy of 97.67%. The proposed system has detected the MAs with higher performance using simple features and holoentropy based decision tree classifier. The proposed system is suitable for early stage detection of DR through the accurate identification of MAs.

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Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images

Cited by 77 publications

References 12 publications

Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM

Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM

Automatic Detection of Microaneurysms and Hemorrhages in Color Eye Fundus Images

An efficient method for early stage detection of diabetic retinopathy

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