Retinal vessel extraction by matched filter with first-order derivative of Gaussian

Zhang, Bob; Zhang, Lin; Zhang, Lei; Karray, Fakhri

doi:10.1016/j.compbiomed.2010.02.008

Cited by 466 publications

(266 citation statements)

References 22 publications

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“…In stage (II), we further enhance the cell structures by vesselness enhancement filtering (step 3), which is used typically to enhance thin elongated structures, such as, e.g. blood vessels, in digital images (Chaudhuri et al, 1989;Zhang et al, 2010;Fraz et al, 2012). Here, we apply a filter kernel, as proposed in Sofka and Stewart (2006).…”

Section: Cell Boundary Segmentation and Region Filteringmentioning

confidence: 99%

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

et al. 2017

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Section: Cell Boundary Segmentation and Region Filteringmentioning

confidence: 99%

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

et al. 2017

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“…In the edgedetection-based methods, one well-known method is utilizing matched filter response (MFR) [2] for vessel detection. Several modified versions of MFR are reported in [4]. A recent development of using multiscale-matched filters for retinal vessel centerline extraction is proposed in [5].…”

Section: Introductionmentioning

confidence: 99%

Retinal Vessel Detection and Measurement for Computer-aided Medical Diagnosis

Wee

2013

J Digit Imaging

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Since blood vessel detection and characteristic measurement for ocular retinal images is a fundamental problem in computer-aided medical diagnosis, automated algorithms/systems for vessel detection and measurement are always demanded. To support computer-aided diagnosis, an integrated approach/solution for vessel detection and diameter measurement is presented and validated. In the proposed approach, a Dempster-Shafer (D-S)-based edge detector is developed to obtain initial vessel edge information and an accurate vascular map for a retinal image. Then, the appropriate path and the centerline of a vessel of interest are identified automatically through graph search. Once the vessel path has been identified, the diameter of the vessel will be measured accordingly by the algorithm in real time. To achieve more accurate edge detection and diameter measurement, mixed Gaussian-matched filters are designed to refine the initial detection and measures. Other important medical indices of retinal vessels can also be calculated accordingly based on detection and measurement results. The efficiency of the proposed algorithm was validated by the retinal images obtained from different public databases. Experimental results show that the vessel detection rate of the algorithm is 100 % for large vessels and 89.9 % for small vessels, and the error rate on vessel diameter measurement is less than 5 %, which are all well within the acceptable range of deviation among the human graders.

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“…al. (Zhang et al, 2010) use the match channel system along the Gaussian subsidiary to extricate the veins. The extraction of veins is the imperative assignment with a specific end goal to analyze the infection through retinal/fundus pictures.…”

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confidence: 99%

Separation of Veins and Arteries for estimating Hypertensive Retinopathy in Fundus Images

Hussain

Faheem

2016

Biomed Res Ther

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Abstract-The determinations of Hypertensive Retinopathy (HR) through retinal pictures turn out being a vital issue today since HR is quickly expanding ailment that is found in eyes. HR happens because of the height of the circulatory strain. The most imperative estimation that is used to analyze HR through retinal pictures is arteriovenous proportion (AVR). This paper depicts a strategy to decide AVR by first section the vessels using match separating method and afterward identify the optic circle to decide the Region of Interest. When the area of interest is discovered, we order the veins into supply routes and veins utilizing Neural Network to decide the AVR. Once the vessels are classified, we separate the arteries and veins based on the color separation. The work is performed by using MATLAB R2014. This paper is partitioned into four segments. Area oneportrays the introduction. Area twodecribes the technique/ systems to decide AVR. Area three depicts the examination of results with some past results. Segment four portrays the conclusion.

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Retinal vessel extraction by matched filter with first-order derivative of Gaussian

Cited by 466 publications

References 22 publications

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

Retinal Vessel Detection and Measurement for Computer-aided Medical Diagnosis

Separation of Veins and Arteries for estimating Hypertensive Retinopathy in Fundus Images

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