Detection and classification of retinal lesions for grading of diabetic retinopathy

Akram, M. Usman; Khalid, Shehzad; Tariq, Anam; Khan, Shoab A.; Azam, Farooque

doi:10.1016/j.compbiomed.2013.11.014

Cited by 235 publications

(47 citation statements)

References 31 publications

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“…Usman Akram, Khalid, Tariq, Khan, and Azam () presented a method based on ensemble classifier to detect the retinal lesions in diabetic retinopathy. The method consisted of three phases; first phase consisted of preprocessing, vessel segmentation, and optic disc localization from retinal image, second phase contained the extraction of candidate lesion by using filter banks and third phase contained feature set formulation for ensemble classifier (GMM and m‐Mediods) to achieve precise classification.…”

Section: Supervised Retinal Vessels Segmentation Methodsmentioning

confidence: 99%

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Akbar

Sharif

Akram

et al. 2019

Microscopy Res & Technique

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Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research. K E Y W O R D S retinal blood vessels, retinal diseases, retinal image databases, supervised techniques, unsupervised techniques

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Section: Supervised Retinal Vessels Segmentation Methodsmentioning

confidence: 99%

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Akbar

Sharif

Akram

et al. 2019

Microscopy Res & Technique

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“…al. [6], automatic detection of lesions was done after detection of blood vessel tree and OD. Enhancement of blood vessels was achieved by inverted green channel followed by Gabor wavelet.…”

Section: Related Workmentioning

confidence: 99%

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Saha¹,

Chowdhury²,

Banerjee³

et al. 2018

NNW

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This paper presents an algorithm for the design of a computer aided diagnosis system to detect, quantify and classify the lesions of non-proliferative diabetic retinopathy as well as dry age related macular degeneration from the fundus retina images. Symptoms of non-proliferative diabetic retinopathy in images consist of bright lesions like hard exudates, cotton wool spots and dark lesions like microaneurysms, hemorrhages. Dry age related macular degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts: image processing, where preprocessed gray scale images are segmented to extract candidate lesions using a combination of Gaussian filtering and multilevel thresholding followed by classification of the different lesions in non-proliferative diabetic retinopathy and age related macular degeneration using perceptron, support vector machine and naïve Bayes classifier. From the comparative performance analysis of the classification techniques, it is observed that comparable results are obtained from single layer perceptron and support vector machine and they both outperform naïve Bayes classifier. The classification accuracy of support vector machine classifier for dark lesion class is 97.13 % and the classification accuracy of single layer perceptron for bright lesion class is 95.13 % with optimal feature set.

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“…Some of the reported developments focus and propose techniques for detecting certain features of diabetic retinopathy, such as microaneurysms in [18–23], exudates in [24–28], haemorrhages in [27, 29] and neovascularisation in [28, 30–32]. …”

Section: Previous Related Workmentioning

confidence: 99%

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing

et al. 2016

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Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

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Detection and classification of retinal lesions for grading of diabetic retinopathy

Cited by 235 publications

References 31 publications

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing

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