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

Rahim, Sarni Suhaila; Jayne, Chrisina; Palade, Vasile; Shuttleworth, James

doi:10.1007/s00521-015-1929-5

Cited by 53 publications

(27 citation statements)

References 35 publications

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“…The automatic detection of such an important sign of diabetic retinopathy, which is microaneurysms, has been proposed by some of researchers. Rahim et al [4] proposed an automatic diabetic retinopathy screening system, followed by a microaneurysms detection system [5]. One of the effective techniques for the localisation and detection of the microaneurysms used by researchers on microaneurysms detection is the Circular Hough Transform (CHT).…”

Section: Related Work On Microaneursyms Detectionmentioning

confidence: 99%

Automatic Detection of Microaneurysms for Diabetic Retinopathy Screening Using Fuzzy Image Processing

Rahim

Palade

Shuttleworth

et al. 2015

Engineering Applications of Neural Networks

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Abstract. Fuzzy image processing was proven to help improve the image quality for both medical and non-medical images. This paper presents a fuzzy techniques-based eye screening system for the detection of one of the most important visible signs of diabetic retinopathy; microaneurysms, small red spot on the retina with sharp margins. The proposed ophthalmic decision support system consists of an automatic acquisition, screening and classification of eye fundus images, which can assist in the diagnosis of the diabetic retinopathy. The developed system contains four main parts, namely the image acquisition, the image preprocessing with fuzzy techniques, the microaneurysms localisation and detection, and finally the image classification. The fuzzy image processing approach provides better results in the detection of microaneurysms.

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Section: Related Work On Microaneursyms Detectionmentioning

confidence: 99%

Automatic Detection of Microaneurysms for Diabetic Retinopathy Screening Using Fuzzy Image Processing

Rahim

Palade

Shuttleworth

et al. 2015

Engineering Applications of Neural Networks

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“…We have also presented several individual systems for the automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening in [6]. The first system highlights the automatic detection of microaneurysms in colour fundus images using segmentation and feature extraction.…”

Section: Previous Related Workmentioning

confidence: 99%

“…In the previous system proposed by [6–8, 33], the colour fundus images are converted into a greyscale image. In the present system, the green channel extraction is used to investigate the difference and capability of the green channel format compared to the greyscale format and other image colour conversion formats.…”

Section: Proposed System: Detection Of Diabetic Retinopathy and Maculmentioning

confidence: 99%

“…Therefore, the implementation of the histogram equalisation by employing fuzzy techniques helps to improve the contrast of the fundus images for better visualisation and detection. The technique called brightness preserving dynamic fuzzy histogram equalisation (BPDFHE) proposed by Sheet et al [4] was found to work well on colour fundus images (see [6–8]) and has been chosen as a preprocessing technique in this proposed system as well.…”

Section: Proposed System: Detection Of Diabetic Retinopathy and Maculmentioning

confidence: 99%

“…Fuzzy techniques can help not only to produce better representation of images and improve the performance analysis but also can help produce a more reliable screening system. Fuzzy image processing has been reported to work well as fuzzy histogram equalisation method by Sheet et al [4], Garud et al [5] and Rahim et al [6–8]. Fuzzy filtering is another fuzzy image processing method, proposed by Patil and Chaudhari [9], Toh et al [10, 11], Kwan [12, 13] and also Rahim et al [7, 8].…”

Section: Introductionmentioning

confidence: 99%

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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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Diabetic retinopathy severity grading employing quadrant‐based Inception‐V3 convolution neural network architecture

Bhardwaj

Jain

Sood

2020

Int J Imaging Syst Tech

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Diabetic retinopathy (DR) accounts in eye‐related disorders due to accumulated damage to small retinal blood vessels. Automated diagnostic systems are effective in early detection and diagnosis of severe eye complications by assisting the ophthalmologists. Deep learning‐based techniques have emerged as an advancement over conventional techniques based on hand‐crafted features. The authors have proposed a Quadrant‐based automated DR grading system in this work using Inception‐V3 deep neural network to extract small lesions present in retinal fundus images. The grading efficiency of the proposed architecture is improved utilizing image enhancement and optical disc removal pipeline along with data augmentation stage. The proposed system yields accuracy of 93.33% with minimized cross‐entropy loss of 0.291. Capability of proposed system is demonstrated experimentally to provide efficient DR diagnosis. The diagnosis ability of the proposed architecture is demonstrated by state‐of‐the‐art comparison with other mainstream convolution neural network models and a maximum improvement of 14.33% is observed.

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

Cited by 53 publications

References 35 publications

Automatic Detection of Microaneurysms for Diabetic Retinopathy Screening Using Fuzzy Image Processing

Automatic Detection of Microaneurysms for Diabetic Retinopathy Screening Using Fuzzy Image Processing

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

Diabetic retinopathy severity grading employing quadrant‐based Inception‐V3 convolution neural network architecture

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