Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

Yavuz, Zafer; Köse, Cemal

doi:10.1155/2017/4897258

Cited by 39 publications

(28 citation statements)

References 27 publications

(38 reference statements)

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“…The second was published in the 1950s by Dr. Paul Tower. He confirmed that each retina in each eye has a different blood vessels pattern and also using photographs he showed that even identical twins have different pattern in their retinas" (Yavuz & Cemal, 2017).…”

Section: Introductionmentioning

confidence: 91%

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Study of Feature Extraction of Retinal Scans

Abdulhamid

Muthomi

2019

Scientific Bulletin

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In this paper, the retina is discussed as part of the feature of extraction of retinal scans for use in security systems as a means of identification. The design system contains a method of image acquisition and processing of the image. A computer system is also incorporated for matching and verifying the image captured to an already present representation of unique features of the retina that are stored as templates for matching and identification. It should then either allow or deny the user depending on the results of the matching process. This paper shows the development of the step undertaken to process the image to the extraction of the features. The high resolution images are taken through a series of image enhancement process before feature extraction technics are applied and before templates are created for future referencing. The main limitation of this process is attributed to capturing the image from the retina. The image obtained may be of poor quality thus making the unique features of the retina unclear.

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Section: Introductionmentioning

confidence: 91%

“…This unique blood vessels patterns form the foundation of the retinal recognition system. "There are two famous studies that confirmed the unique blood vessel pattern found in the retina" (Yavuz & Cemal, 2017):…”

Section: Introductionmentioning

confidence: 98%

Study of Feature Extraction of Retinal Scans

Abdulhamid

Muthomi

2019

Scientific Bulletin

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“…Bharkad used top hat, a morphological operator with three different structuring elements [24]. Yavuz et al enhanced the retinal image using Gabor, Frangi, and Gaussian filters, followed by the use of top hat transform and clustering mechanism for segmenting the blood vessels [25].…”

Section: Related Workmentioning

confidence: 99%

Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach

et al. 2019

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A hybrid segmentation algorithm is proposed is this paper to extract the blood vesselsfrom the fundus image of retina. Fundus camera captures the posterior surface of the eye and thecaptured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinalhaemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysisof vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the fieldof ophthalmology. It is derived from the literature review that no unique segmentation algorithm issuitable for images of different eye-related diseases and the degradation of the vessels differ frompatient to patient. If the blood vessels are extracted from the fundus images, it will make thediagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithmexclusively for the extraction of blood vessels from the fundus image. The proposed algorithm ishybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement(MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, thearea-based morphological operator is applied to highlight the blood vessels. To validate theproposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus(HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments theblood vessels with more accuracy than the existing algorithms.

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“…two-dimensional matched filter response (2D MFR)), morfologinis metodas, kraujagyslių sekimo metodas. Prie mašininio mokymosi metodų priskiriami k-NN, SVM, Bayesian decision rule, Fuzzy C-means, K-means (Yavuz, Köse, 2017) ir dirbtiniai neuroniniai tinklai.…”

Section: Akies Vaizdų Semantinis Segmentavimasunclassified

Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

Toliušis

Kurasova

Bernatavičienė

2019

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This article reviews the problems of eye bottom fundus analysis and semantic segmentation algorithms used to distinguish the eye vessels and the optical disk. Various diseases, such as glaucoma, hypertension, diabetic retinopathy, macular degeneration, etc., can be diagnosed through changes and anomalies of the vesssels and optical disk. Convolutional neural networks, especially the U-Net architecture, are well-suited for semantic segmentation. A number of U-Net modifications have been recently developed that deliver excellent performance results.

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Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

Cited by 39 publications

References 27 publications

Study of Feature Extraction of Retinal Scans

Study of Feature Extraction of Retinal Scans

Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach

Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

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