Automated segmentation of blood vessels for detection of proliferative diabetic retinopathy

Akram, M. Usman; Jamal, Ibaa; Tariq, Anam; Imtiaz, Junaid

doi:10.1109/bhi.2012.6211553

Cited by 27 publications

(14 citation statements)

References 12 publications

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“…Akram et al [14] proposed a method for segmenting the blood vessels in retinal images for detecting proliferative diabetic retinopathy. In proliferative diabetic retinopathy, PDR, new blood vessels starts growing (called neovascularization) in the retina which hinders the vision severely.…”

Section: Iiimethodsmentioning

confidence: 99%

A survey on blood vessel segmentation methods in retinal images

Singh

Kaur

2015

2015 International Conference on Electronic Design, Computer Networks &Amp; Automated Verification (EDCAV)

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Diabetic Retinopathy occurs in patients who suffer from diabetes for many years and as a result of which the vision gets affected. The affect can be low to severe depending on the extent to which the disease has occurred. There are two stages of the disease. The early stage is Non proliferative diabetic retinopathy (NPDR) and later is Proliferative diabetic retinopathy (PDR). In NPDR, various problems may occur such as macular edema which is swelling in the central retina and retinal ischemia which occurs due to poor blood flow. In PDR, the advanced stage of NPDR, new blood vessels starts growing in the retina known as neovascularization. The extraction of retinal blood vessels if done at early stages can be very helpful in diagnosing the severity of the disease and accordingly the treatment can be followed. In later stages, treatment is not very effective. In this paper various blood vessel segmentation techniques are discussed. Besides segmentation techniques, retinal image enhancement techniques are also discussed. The evaluation of techniques is done on publically available databases DRIVE and STARE. These databases contain retinal images along with the ground truth images which are accurately marked by the experts for the purpose of evaluation of the techniques. The paper also discusses various metrics which are frequently used for the evaluation of image segmentation techniques.

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Section: Iiimethodsmentioning

confidence: 99%

A survey on blood vessel segmentation methods in retinal images

Singh

Kaur

2015

2015 International Conference on Electronic Design, Computer Networks &Amp; Automated Verification (EDCAV)

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“…The automatic detection of exudates, which are defined as small white or yellowish-white deposits with sharp margins [36], has been proposed in [38, 48-49, 53, 55]. Meanwhile, researchers have proposed various methods for the detection of other signs of diabetic retinopathy, such as hemorrhages [53][54] as well as neovascularization, caused by abnormal new vessels [50][51][52]55]. …”

Section: Previous Related Workmentioning

confidence: 99%

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening

Rahim

Jayne

Palade

et al. 2015

Neural Comput & Applic

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“…The disease progresses over years or decades to affect central vision and hence early detection can be a preventive step to stop its further growth. M any methods have been proposed in order to get an accurate blood vessel detection and segmentation in fundus images like multilayered thresholding technique to segment the vessels [3], usage of scale and orientation selective Gabor filters to detect the abnormal blood vessels [4]. The previous automation techniques were limited to a real-time quality measurement of the captured images [6].…”

Section: Introductionmentioning

confidence: 99%

An Automated Approach to Differentiate a Normal eye from a Defective Eye

Das¹,

Saha²,

Deb³

et al. 2013

IJCA

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Detection of basic differentiating characteristics of eye diseases from the images of the retina can be a good approach as a low-cost method for broad-bas ed initial screening. For example early diabetic retinopathy detection enables application of laser therapy treatment in order to prevent or delay loss of vision. The paper has referenced Diabetic retinopathy and Retinitis pigmentosa for analysis. Automated approach for detection of microaneurysms in digital color fundus photographs helps ophthalmologist to detect the emergence of its initial symptoms and determine the next action step for the patient. A similar mechanism for automated early disease detection method with respect to the features of the normal eye is proposed. The detection algorithm features identification of black pigments like minute features, microaneurysm and exudate detection and these features extracted can prove to a greater extent as ready instances for defectiveness. A number of images along with the feedback and consultation from the ophthalmologist in this area of medical science has proved to be a great help towards the observation as derived from this mechanism and discussed in the later end of this paper. The proposed mechanism can be extended up to the limit of supervised learning so as to automate the practical feedbacks as obtained from the practitioners.

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Automated segmentation of blood vessels for detection of proliferative diabetic retinopathy

Cited by 27 publications

References 12 publications

A survey on blood vessel segmentation methods in retinal images

A survey on blood vessel segmentation methods in retinal images

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening

An Automated Approach to Differentiate a Normal eye from a Defective Eye

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