Multi-scale retinal vessel segmentation using line tracking

Vlachos, M.; Dermatas, Evangelos

doi:10.1016/j.compmedimag.2009.09.006

Cited by 213 publications

(95 citation statements)

References 18 publications

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“…This method claimed that it could significantly reduce the image variation [24]. Vlachos et al used this model for background normalization [25]. Goatman et al proposed a method for contrast enhancement using image contrast to cover the whole pixel coverages for normalization [26] …”

Section: Image Normalizationmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review

Aras¹,

Lestari²,

Nugroho³

et al. 2016

CST

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Diabetic detinopathy (DR) is effect of diabetes mellitus to the human vision that is the major cause of blindness. Early diagnosis of DR is an important requirement in diabetes treatment. Retinal fundus image is commonly used to observe the diabetic retinopathy symptoms. It can present retinal features such as blood vessel and also capture the pathologies which may lead to DR. Blood vessel is one of retinal features which can show the retina pathologies. It can be extracted from retinal image by image processing with following stages: preprocessing, segmentation, and post-processing. This paper contains a review of public retinal image dataset and several methods from various conducted researches. All discussed methods are applicable to each researcher cases. There is no further analysis to conclude the best method which can be used for general cases. However, we suggest morphological and multiscale method that gives the best accuracy in segmentation.

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Section: Image Normalizationmentioning

confidence: 99%

“…Vlacos et al [46] proposed multi sale line tracking for blood vessel segmentation. Line tracking is initialized in various scales to accommodate various vessel widths.…”

Section: Multiscalementioning

confidence: 99%

Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review

Aras¹,

Lestari²,

Nugroho³

et al. 2016

CST

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show abstract

“…We evaluated precision of the methods based only on the shadow detection ( [10], [18]) and compared them to our two proposed solutions as well as to the line tracking method designed for color fundus images [19]. First experiment was set to evaluate the accuracy of vessels segmentation in the lateral direction.…”

Section: B Vessels Detection In the Lateral Directionmentioning

confidence: 99%

Volumetric segmentation of human eye blood vessels based on OCT images

Stankiewicz

Marciniak

Dąbrowski

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we present a method for volumetric segmentation of retinal vessels based on 3D OCT images of human macula. The proposed hybrid method is comprised of two steps: detailed extraction of superficial blood vessels indicators visible in 2D projection of retina layers followed by an axial inspection of inner retina to determine exact depth position of each vessel. The segmentation procedure is improved by application of block-matching and 4D filtering (BM4D) algorithm for noise reduction. The 3D reconstruction of vascular structure was performed for 10 normal subjects examined with Avanti AngioVue OCT device. The automated segmentation results were validated against the manual segmentation performed by an expert giving the accuracy of 95.2%.

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“…Martínez-Pérez et al (1999) used a combination of scale space analysis and region growing to segment the vessel network. Vlachos and Dermatas (2010) proposed a retinal vessel segmentation method based on multi-scale line-tracking procedure and morphological post-processing. Wang et al (2013) proposed multi-wavelet kernels and multi-scale hierarchical decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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Multi-scale retinal vessel segmentation using line tracking

Cited by 213 publications

References 18 publications

Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review

Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review

Volumetric segmentation of human eye blood vessels based on OCT images

Automatic Retinal Vessel Detection and Tortuosity Measurement

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