A generalized method for the detection of vascular structure in pathological retinal images

Kaur, Jaskirat; Mittal, Deepti

doi:10.1016/j.bbe.2016.09.002

Cited by 39 publications

(14 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although none of the public data sets contains ROP images. One such recent segmentation work is the research by Kaur and Mittal 11 that remarks there are fewer proposals researching the images of the pathological retina. In this article, the segmentation of vascular structure is studied and a novel generalized method is proposed to segment vascular network in pathological images.…”

Section: Related Workmentioning

confidence: 99%

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Intriago-Pazmiño

Ibarra-Fiallo

Crespo³

et al. 2020

Health Informatics J

View full text Add to dashboard Cite

Retinopathy of prematurity is a disease that can affect premature or in similar conditions babies. For diagnosing of retinopathy of prematurity, the infant is examined as soon as possible. Due to the nature of the examination, the images obtained are poor in quality. This article presents an automated method for processing fundus images to improve the visibility of the vascular network. The method includes several processing tasks whose parameters are predicted using an artificial neural network. A set of 88 clinical images were used in this work. The performance of our proposal is efficient, and the average processing time was 42 ms. The method was assessed using both the contrast improvement index and expert opinions. The contrast improvement index average was 2; this means the processed image successfully improved its contrast. Three pediatric ophthalmologists validated the proposed method and agreed that the visual enhancement can help observe clearly the retinal vessels.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Intriago-Pazmiño

Ibarra-Fiallo

Crespo³

et al. 2020

Health Informatics J

View full text Add to dashboard Cite

show abstract

“…• SNDRSP Singapore National DR Screening Program collected 197,085 fundus images in 2013 and 2010. This dataset was exclusively used by [51] for the purpose of research related to diabetic retinopathy and eye diseases. • JMU In this dataset, there are 9939 digital fundus images.…”

Section: ) Private Datasetsmentioning

confidence: 99%

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

et al. 2020

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a timeconsuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology.

show abstract

“…Additionally, the detection of geometrical-based vessel features and secular changes of vessels, as shown in Figure 4, assists less experienced doctors in the accurate diagnosis of DR. Retinal vessel segmentation methods in the literature can be divided into three groups: Supervised-based, unsupervised-based, and mathematical morphology-based methods. Kaur and Mittal in [42] developed a vascular detection system using fundus images. Preprocessing was initially done to normalize the images from low contrast and non-uniform illumination.…”

Section: Blood Vessel Extractionmentioning

confidence: 99%

“…A comprehensive review of supervised-related, unsupervised-related, image processing-related, and data mining-related algorithms is presented in [45][46][47] with high vessel segmentation results. Kaur and Mittal in [42] developed a vascular detection system using fundus images. Preprocessing was initially done to normalize the images from low contrast and non-uniform illumination.…”

Section: Blood Vessel Extractionmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

View full text Add to dashboard Cite

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.

show abstract

A generalized method for the detection of vascular structure in pathological retinal images

Cited by 39 publications

References 36 publications

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Contact Info

Product

Resources

About