An efficient blood vessel detection algorithm for retinal images using local entropy thresholding

Chanwimaluang, Thitiporn; Fan, Guoliang

doi:10.1109/iscas.2003.1206162

Cited by 137 publications

(100 citation statements)

References 4 publications

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“…An example of image-based vascular network reconstructed by our group using matched filters and local entropy thresholding, as in [7], is reported in Figure 11. Image extraction was performed on retinal images obtained from those archived in the DRIVE data base and represents images from diabetic subjects [43].…”

Section: Influence Of Vascular Architecturementioning

confidence: 99%

Effect of ocular shape and vascular geometry on retinal hemodynamics: a computational model

Dziubek

Guidoboni

Harris

et al. 2015

Biomech Model Mechanobiol

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Thistleton, W. (2016Effect of ocular shape and vascular geometry on retinal hemodynamics: a computational modelReceived: date / Accepted: date Abstract A computational model for retinal hemodynamics accounting for ocular curvature is presented. The model combines: (i) a hierarchical Darcy model for the flow through small arterioles, capillaries and small venules in the retinal tissue, where blood vessels of different size are comprised in different hierarchical levels of a porous medium; and (ii) a one-dimensional network model for the blood flow through retinal arterioles and venules of larger size. The non-planar ocular shape is included by: (i) defining the hierarchical Darcy flow model on a two-dimensional curved surface embedded in the three-dimensional space; and (ii) mapping the simplified one-dimensional network model onto the curved surface. The model is solved numerically using a finite element method in which spatial domain and hierarchical levels are discretized separately. For the finite element method we use an exterior calculus based implementation which permits an easier treatment of non-planar domains. Numerical solutions are verified against suitably constructed analytical solutions. Numerical experiments are performed to investigate how

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Section: Influence Of Vascular Architecturementioning

confidence: 99%

Effect of ocular shape and vascular geometry on retinal hemodynamics: a computational model

Dziubek

Guidoboni

Harris

et al. 2015

Biomech Model Mechanobiol

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“…The training data or hand labelled ground truths are not needed for the design of algorithm in these approaches. These approaches are matched filter-based (Chaudhuri et al;1989;Hoover et al;Chanwimaluang and Fan;Cinsdikici and Aydın;2009;Zhang et al;Chakraborti et al;, scale space-based (Martínez-Pérez et al;Vlachos and Dermatas;Wang et al;, tracking-based , model-based (Szpak and Tapamo;Xiao et al;, adaptive thresholding-based (Jiang and Mojon;Cornforth et al;2005;Akram and Khan;, mathematical morphology-based (Zana and Klein;Mendonca and Campilho;Jiménez et al;Miri and Mahloojifar;2012a) and clustering-based (Tolias and Panas;1998;Yang et al;Kande et al;2011a,b;Sun et al;Saffarzadeh et al;. Chaudhuri et al (1989) implemented matched filter response (MFR) by initially approximating the intensity of gray-level profiles of the cross-sections of retinal vessels using a Gaussian shaped curve.…”

Section: Introductionmentioning

confidence: 99%

“…While compared to the technique used in (Chaudhuri et al;1989) where a basic thresholding of an MFR was used, the method proposed in (Hoover et al; reduced the false positive rate by as much as 15 times. Chanwimaluang and Fan (2003) proposed the combination of matched filter and entropy for the segmentation of retinal vessels. The performance measure of the proposed technique was only visual.…”

Section: Introductionmentioning

confidence: 99%

Automatic Retinal Vessel Detection and Tortuosity Measurement

Mapayi¹,

Tapamo²,

Viriri³

et al. 2016

Image Anal Stereol

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As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents the study on the combination of difference image and k-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed k-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.

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“…In this case, segmentation of blood vessel in Fundus plays important rule in detecting eye blood vessel damage [5]. The segmentation results such as length, wide, shape of blood vessel branch will assist to determine kind of the diabetic retinopathy diseases [6]. The problem occurred on medical world related to blood vessel on Fundus is difficult to segment manually blood vessel, it is require high cost and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Matrix Mask Overlapping and Convolution Eight Directions for Blood Vessel Segmentation on Fundus Retinal Image

2014

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An efficient blood vessel detection algorithm for retinal images using local entropy thresholding

Cited by 137 publications

References 4 publications

Effect of ocular shape and vascular geometry on retinal hemodynamics: a computational model

Effect of ocular shape and vascular geometry on retinal hemodynamics: a computational model

Automatic Retinal Vessel Detection and Tortuosity Measurement

Matrix Mask Overlapping and Convolution Eight Directions for Blood Vessel Segmentation on Fundus Retinal Image

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