Boosting sensitivity of a retinal vessel segmentation algorithm

Khan, Muhammad Mushtaq; Khan, Tariq M.; Soomro, Toufique Ahmed; Mir, Nighat; Gao, Junbin

doi:10.1007/s10044-017-0661-4

Cited by 30 publications

(13 citation statements)

References 34 publications

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“…A matching filter with signed integers is used to improve the difference among both vascular and nonvascular pixels. A number of contrast-sensitive approaches for improving the sensitivity of existing retinal vascular segmentation algorithms are implemented [28,29].…”

Section: Related Workmentioning

confidence: 99%

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Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

David

Mahesh

Kumar

et al. 2022

Mathematical Problems in Engineering

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A color fundus image is a photograph obtained using a fundus camera of the inner wall of the eyeball. In the image, doctors may see changes in the retinal vessels, which can be used to diagnose various dangerous disorders such as arteriosclerosis, some macular degeneration related to age, and glaucoma. To diagnose certain disorders as early as possible, automatic segmentation of retinal arteries is used to help the doctors. Also, it is a challenge for the medical community to analyze the image with the right procedure to diagnose the disorders with high accuracy. Furthermore, this will help the doctor to make the right decision on effective treatment. Hence, the authors have implemented an enhanced architecture called U-Net to segment retinal vessels in this paper. The proposed conventional U-Net permits using all the accessible spatial setting information by adding the multiscale input layer and a thick square to the conventional U-Net in terms of improving the accuracy level of image segmentation. It achieved 95.6% accuracy with a comparatively traditional U-Net model. Moreover, the segmentation results have proved that the proposed approach outperformed in detecting most complex low-contrast blood vessels even when they are very thin. The task of segmenting vessels in retinal images is known as retinal vessel segmentation. Blood vessel density can be assessed using dense pixel values. Data augmentation and analytics play a major role in building the true value of eye blood vessels for medical diagnosis. The proposed method is very promising in the automatic segmentation of retinal arteries.

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Section: Related Workmentioning

confidence: 99%

“…erefore, deep learning is being used extensively in picture segmentation. A full network channel for segmentation can be implemented for obtaining good results [29]. e implemented model aids in the retention of a greater number of picture features as well as the restoration of the image for a longer period of time.…”

Section: Related Workmentioning

confidence: 99%

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

David

Mahesh

Kumar

et al. 2022

Mathematical Problems in Engineering

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“…Similar methods were employed using MF [ 27 – 32 ], combined filters [ 33 ], COSFIRE filters [ 3 , 5 , 34 – 36 ], Gaussian filters [ 37 ], wavelet filters [ 38 ], and Frangi's filter [ 39 ]. The MM-based approaches are utilized for isolating retinal image segments such as optic disk, macula, fovea, and vasculature.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation

Khan

Siddique

Mazzara

2020

BioMed Research International

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Retinal vessel segmentation (RVS) is a significant source of useful information for monitoring, identification, initial medication, and surgical development of ophthalmic disorders. Most common disorders, i.e., stroke, diabetic retinopathy (DR), and cardiac diseases, often change the normal structure of the retinal vascular network. A lot of research has been committed to building an automatic RVS system. But, it is still an open issue. In this article, a framework is recommended for RVS with fast execution and competing outcomes. An initial binary image is obtained by the application of the MISODATA on the preprocessed image. For vessel structure enhancement, B-COSFIRE filters are utilized along with thresholding to obtain another binary image. These two binary images are combined by logical AND-type operation. Then, it is fused with the enhanced image of B-COSFIRE filters followed by thresholding to obtain the vessel location map (VLM). The methodology is verified on four different datasets: DRIVE, STARE, HRF, and CHASE_DB1, which are publicly accessible for benchmarking and validation. The obtained results are compared with the existing competing methods.

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“…Diabetes can cause many diseases in human body, such as kidney failure, heart issues, stroke and loss of vision. Diabetic retinopathy (DR) is a diabetic complication that affects eyes and is the main cause of blindness in diabetes triggered diseases [1], [2], [3], [4]. Diabetic retinopathy occurs due to long-term cumulative damage of small retinal vessels in the retina.…”

Section: Introductionmentioning

confidence: 99%

A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening

Manan¹,

Saadat²,

Arsalan³

et al. 2022

Preprint

Self Cite

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Diabetic retinopathy refers to the pathology of the retina induced by diabetes and is one of the leading causes of preventable blindness in the world. Early detection of diabetic retinopathy is critical to avoid vision problem through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of lesion and low contrast of the images. Thus, computer-assisted diagnosis of diabetic retinopathy based on the detection of red lesions is actively being explored recently. In this paper, we present a convolutional neural network with residual skip connection for the segmentation of exudates in retinal images. To improve the performance of network architecture, a suitable image augmentation technique is used. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. Comparative performance analysis of three benchmark databases: HEI-MED, E-ophtha, and DiaretDB1 is presented. It is shown that the proposed method achieves accuracy (0.98, 0.99, 0.98) and sensitivity (0.97, 0.92, and 0.95) on E-ophtha, HEI-MED, and DiaReTDB1, respectively.

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Boosting sensitivity of a retinal vessel segmentation algorithm

Cited by 30 publications

References 34 publications

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation

A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening

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