A new supervised retinal vessel segmentation method based on robust hybrid features

Aslani, Shahab; Sarnel, Haldun

doi:10.1016/j.bspc.2016.05.006

Cited by 121 publications

(63 citation statements)

References 42 publications

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“…For example, changes in the diameter of the vessel and development of neovascularization indicate diabetic retinopathy, while a decrease in the ratio of the diameter of the retinal arteries to the diameter of the vessels indicates hypertension. Another important feature of ocular blood vessels is that it is the only vein region in the human blood circulation system that can be examined from outside without requiring intervention [3]. Ophthalmologists diagnose patients by manually examining the status of blood vessels on retinal images obtained with high-resolution fundus cameras [2].…”

Section: Introductionmentioning

confidence: 99%

“…Ophthalmologists diagnose patients by manually examining the status of blood vessels on retinal images obtained with high-resolution fundus cameras [2]. Although manual segmentation has high accuracy, it is a tedious, timeconsuming operation that can be impossible in the presence of high-volume retinal data [3]. Thus, medical imaging has become a significant tool in recent times [4].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the segmentation of blood vessels is difficult because the width, brightness and shape of blood vessels are variable. Thus, it is imperative to develop an effective image processing technique for an accurate automatic segmentation [3]. Different image processing techniques are used for retinal blood vessel segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised methods are rule-based and specify vein regions using the default rules for vessels. Unsupervised learning includes vein tracking, matched filtering, morphological processing, modelbased algorithms and multi-scale analysis [3]. Supervised learning is more time-consuming and computationally expensive, as it requires training of complex classifiers that work with large data with different features [3].…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised learning includes vein tracking, matched filtering, morphological processing, modelbased algorithms and multi-scale analysis [3]. Supervised learning is more time-consuming and computationally expensive, as it requires training of complex classifiers that work with large data with different features [3]. If we give an example to unsupervised methods, a morphological component analysis (MCA) algorithm is used for retinal blood vessel segmentation by Imani et al and it is aimed to prevent the formation of false positive vessel pixels in the area of diabetic lesions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Kaya

Sarıtaş

Ceylan

2017

ijisae

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Abstract:In this study, an unsupervised retina blood vessel segmentation process was performed on the gray level images with principal component analysis (PCA) and the green channel of the RGB image, which most clearly shows retinal vessels and the results were compared. The images were improved for a good segmentation by using contrast-limited adaptive histogram equalization (CLAHE), color intensity adjustment, morphological operations and median and Gaussian filtering. Retinal vessel structures were segmented with top-hat and bot-hat morphological operations and converted to binary image by using global thresholding. The average accuracy rate obtained for the gray level image with PCA after the study was 0.9443, while the average accuracy rate obtained for the green channel was 0.9685. The study was performed using 40 images in the DRIVE data set which is one of the most common retina data sets known.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Kaya

Sarıtaş

Ceylan

2017

ijisae

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Illumination normalized based technique for retinal blood vessel segmentation

Dash¹,

Senapati

Sahu

et al. 2020

Int J Imaging Syst Tech

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The Retinal image carries important information about the health of the sensory part of the visual system. In this paper, a new approach is suggested by utilizing the homomorphic filter integrated with Contrast limited adaptive histogram equalization (CLAHE) method for the illumination normalization and contrast enhancement of the retinal images. Then segmentation is done through several steps by using the existing methods such as morphological filtering, a second derivative operator that is followed by a final morphological filtering stage and hysteresis thresholding. The suggested method is verified on DRIVE and CHASE‐DB1 databases and has average accuracy of 72.03% and 64.54%, accordingly. The obtained results demonstrate that the proposed approaches achieve higher accuracy than the traditional method. The suggested approach not only contributes to the successful result, but also minimizes the computing time.

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A vessel segmentation technique for retinal images

Iqbal

Riaz

Ghafoor

et al. 2020

Int J Imaging Syst Tech

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Segmentation of the human eye retinal image is an essential step for proper examination and diagnosis by the ophthalmologists or eye care specialists. A technique for vessel segmentation of retinal images is proposed. Retinal images are mostly low‐light images, which are first processed for enhancement of light as well as for detail amplification. Illumination of low‐light images is enhanced, and details are amplified using content‐adaptive filters. For extraction of vessels from retinal images, after low‐light and detail enhancement, the B‐cosfire filter is modified by including extraction of details and small elements, which may otherwise be ignored. A modified B‐cosfire filter is used to extract vessels while minimizing false edges and halo artifacts. The morphological opening is performed to crop vessels that are falsely segmented. The technique is contrasted with other existing methods in terms of accuracy using publicly available datasets. The proposed technique is tested on STARE, CHASE‐DB1, and DRIVE databases. The outcome of the proposed procedure has better accuracy, preserved edges, minimum noise, and artifacts than the state‐of‐the‐art techniques.

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A new supervised retinal vessel segmentation method based on robust hybrid features

Cited by 121 publications

References 42 publications

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Illumination normalized based technique for retinal blood vessel segmentation

A vessel segmentation technique for retinal images

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