Blood Vessel Detection via a Multi-window Parameter Transform

Estabridis, Katia; deFigueiredo, R.J.P.

doi:10.1109/cbms.2006.63

Cited by 14 publications

(6 citation statements)

References 11 publications

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“…Finally, by performing a logical AND between the binary window and the approximate representation, a precise representation of the fragment of the exposed vessel is obtained (Figure 3). [10][11][12][13][14][15]…”

Section: Validation Of the Vessel With Its Refinementmentioning

confidence: 99%

Diagnosis of alzheimer disease through detection of eyes retinal blood vessels

2020

MOJABB

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Today, Alzheimer's disease is rapidly growing and spreading. The disease is caused by the accumulation of Amyloid plaque deposits, thereby destroying the connection between nerve cells and the formation of neurofibrillary nods. According to recent research, one of the ways to diagnose Alzheimer's disease is by reflecting light into the patient's retinal blood vessels and using a machine vision system. Therefore, in this study, we have attempted to introduce a method to detect human retinal blood vessels to improve the disease. In the above method, using DRIVE bank images, they are first clustered and after applying the Unsharp filter and Radon conversion, the vessel is diagnosed so that early detection of Alzheimer's disease by counting the number of vessels present.

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Section: Validation Of the Vessel With Its Refinementmentioning

confidence: 99%

Diagnosis of alzheimer disease through detection of eyes retinal blood vessels

2020

MOJABB

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“…Therefore, the exact extraction of the blood vessels from the retinal images necessitates an algorithm and instrument which reduce the dependency on the physician's skill level and eliminate the error factors. In the most common methods used to extract the blood vessels, tracking-based [1], classifying-based [2] and window-based [3] methods can be referenced. Because of the variability of light reflection coefficients in different parts of the retina layer and the defects which exist in imaging systems, there occurs very non-uniform illumination in the retinal images, which impairs modeling the blood vessels in window-based methods and tracking in tracking-based methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel and Efficient Method to Extract Blood Vessels from Retinal Images

Shahbeig¹,

Helfroush²

2016

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The retinal images contain vital features which are important in the analysis of the retina for various medical applications or human identification. In this manuscript, an Adaptive Weighted Morphological Functor (AWMF) method is proposed for the extraction of blood vessels. The method considers the dispersion of the blood vessel in various directions in the retinal images with prior knowledge of the approximate direction of the edges. The proposed blood vessels extraction algorithm, using principle component analysis (PCA) technique and estimation of the brightness and contrast of the background, effectively eliminates interference of regions with intense brightness structure from the background light distribution. Then the method selects a proper way to modify the brightness distribution of the background, regardless of the destructive effect of brightness structures such as optical disc (OD). The experimental results indicate that the proposed algorithm achieves an accuracy rate of 97.11% for the blood vessels extraction on the famous databases, DRIVE.

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“…Hence, the exact extraction of the blood vessels from the retinal images necessitates using an algorithm and instruments that reduce the dependency on the functor and eliminate the error factors. Among the most common methods used to extract the blood vessels, tracking-based [1], classifying-based [2], and window-based [3] methods can be referenced. Because of the variability of the light reflection coefficient in different parts of the retina layer, which are also due to the defects in imaging systems, there occurs very nonuniform illumination in the retinal images, which impairs modeling the blood vessels in window-based methods and tracking in tracking-based methods.…”

Section: Introductionmentioning

confidence: 99%

Retinal image analysis using multidirectional functors based on geodesic conversions

Shahbeig¹

2014

Turk J Elec Eng & Comp Sci

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Abstract:A retinal image contains vital information. Extracting these features is the first and most important step in the analysis of retinal images for various applications of medical or human recognition. In this study, a morphologicalbased blood vessel extraction algorithm using adaptive-local analysis from colored retinal images is proposed. In this algorithm, by applying the appropriate morphology functors and local histogram stretching on the retinal images, the brightness of the images is considerably uniformed. Furthermore, curvelet transform (CT) is used to enhance the retinal images by highlighting the edges of the images in various scales and directions and by the adaptive and local improving of the CT coefficients. Since the blood vessels in retinal images are distributed in various directions, we use the morphology functors with multidirectional structure elements to extract the blood vessels from the retinal images.Geodesic conversion-based morphology functors are used to properly refine the appeared frills with a size smaller than those of the arterioles in the images. Finally, by locally applying the connected component analysis in the images and locally applying an adaptive filter on the connected components, all of the residual frills are refined from the images.The obtained results of the proposed algorithm show that the blood vessels are extracted from the background of the images with high accuracy, which in turn shows the high ability of the proposed algorithm in extracting the retinal blood vessels.

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Blood Vessel Detection via a Multi-window Parameter Transform

Cited by 14 publications

References 11 publications

Diagnosis of alzheimer disease through detection of eyes retinal blood vessels

Diagnosis of alzheimer disease through detection of eyes retinal blood vessels

A Novel and Efficient Method to Extract Blood Vessels from Retinal Images

Retinal image analysis using multidirectional functors based on geodesic conversions

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