Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey

Soomro, Toufique Ahmed; Gao, Junbin; Khan, Tariq M.; Hani, Ahmad Fadzil M.; Khan, Muhammad Mushtaq; Paul, Manoranjan

doi:10.1007/s10044-017-0630-y

Cited by 55 publications

(28 citation statements)

References 115 publications

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“…Using the haze-lines, a regularized inverse algorithm is presented to make the image haze-free. We adopted this inverse process for its contrast- After de-hazing, the green channel is selected, since vessels are more prominent in this channel [19,20]. The effect of vessels is first minimized using morphological dilation operation.…”

Section: Od Localizationmentioning

confidence: 99%

A region growing and local adaptive thresholding-based optic disc detection

et al. 2020

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Automatic optic disc (OD) localization and segmentation is not a simple process as the OD appearance and size may significantly vary from person to person. This paper presents a novel approach for OD localization and segmentation which is fast as well as robust. In the proposed method, the image is first enhanced by de-hazing and then cropped around the OD region. The cropped image is converted to HSV domain and then V channel is used for OD detection. The vessels are extracted from the Green channel in the cropped region by multi-scale line detector and then removed by the Laplace Transform. Local adaptive thresholding and region growing are applied for binarization. Furthermore, two region properties, eccentricity, and area are then used to detect the true OD region. Finally, ellipse fitting is used to fill the region. Several datasets are used for testing the proposed method. Test results show that the accuracy and sensitivity of the proposed method are much higher than the existing state-of-the-art methods.

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Section: Od Localizationmentioning

confidence: 99%

A region growing and local adaptive thresholding-based optic disc detection

et al. 2020

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“…Finally, some other layers convert these segments into the classification/recognition of images. All these layers learn features from the input data using learning procedures and without expert intervention [135][136][137][138].…”

Section: Latest Trendsmentioning

confidence: 99%

“…Each neuron outcome is then mixed to maintain overlapping among input areas to better represent the original image information. This procedure is pursued for all layers until desirable results are achieved [135][136][137][138][139][140][141][142]. [145] CNN model Detection of exudates -- [113] Multiscale and CNN Detection of fovea and OD -AC: 97%…”

Section: Latest Trendsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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“…Unsupervised methods, on the other hand, operate independently of human assistance or prior training, and thus come with their own set of pros and cons. Automatic vessel detection has reached considerably high accuracy rates, yet invariably all methods suffer at detecting tiny vessels in more challenging images with varying sensitivity rates [2]. Complicated vessel geometry and retinal pathologies such as glaucoma, hypertension, DRP, and Age-related Macular Degeneration (AMD) further degrade the performance of the vessel segmentation techniques [3], [4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

et al. 2019

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Vessel segmentation has come a long way in terms of matching the experts at detection accuracy, yet there is potential for further improvement. In this regard, the accurate detection of vessels is generally more challenging due to the high variations in vessel contrast, width, and the observed noise level. Most vessel segmentation strategies utilize contrast enhancement as a preprocessing step, which has an inherent tendency to aggravate the noise and therefore, impede accurate vessel detection. To alleviate this problem, we propose to use the state-of-the-art Probabilistic Patch-Based (PPB) denoiser within the framework of an unsupervised retinal vessel segmentation strategy based on the Frangi filter. The PPB denoiser helps preserve vascular structure while effectively dealing with the amplified noise. Also, the modified Frangi filter is evaluated separately for tiny and large vessels, followed by individual segmentation and linear recombination of the binarized outputs. This way, the performance of the modified Frangi filter is significantly enhanced. The performance evaluation of the proposed method is evaluated on two recognized open-access datasets, viz: DRIVE and STARE. The proposed strategy yields competitive results for both preprocessing modalities, i.e., Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generalized Linear Model (GLM). The performance observed for CLAHE over DRIVE and STARE datasets is (Sn = 0.8027, Acc = 0.9561) and (Sn = 0.798, Acc = 0.9561), respectively. For GLM, it is observed to be (Sn = 0.7907, Acc = 0.9603) and (Sn = 0.7860, Acc = 0.9583) over DRIVE and STARE datasets, respectively. Furthermore, based on the conducted comparative study, it is established that the proposed method outperforms various notable vessel segmentation methods available in the existing literature. INDEX TERMS Image denoising, image segmentation, modified Frangi filter, probabilistic patch-based denoiser, retinal vessels.

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Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey

Cited by 55 publications

References 115 publications

A region growing and local adaptive thresholding-based optic disc detection

A region growing and local adaptive thresholding-based optic disc detection

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

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