An effective retinal blood vessel segmentation method using multi-scale line detection

Nguyen, Uyen T. V.; Bhuiyan, Alauddin; Park, Laurence A. F.; Ramamohanarao, Kotagiri

doi:10.1016/j.patcog.2012.08.009

Cited by 449 publications

(377 citation statements)

References 31 publications

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“…Significant improvement is still needed even though there are many methods have been proposed. This is due to the limitations of the methods which include poor segmentation when merging of close vessels, missing of small vessels, detection of false vessels at the optic disk and many more [6].…”

Section: Retinal Blood Vessel Segmentation Using Ensemble Of Single Omentioning

confidence: 99%

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Retinal Blood Vessel Segmentation Using Ensemble of Single Oriented Mask Filters

Kasmin

Abdullah

Prabuwono

2017

IJECE

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This paper describes a method on segmentation of blood vessel in retinal images using supervised approach. Blood vessel segmentation in retinal images can be used for analyses in diabetic retinopathy automated screening. It is a very exhausting job and took a very long time to segment retinal blood vessels manually. Moreover these tasks also requires training and skills. The strategy involves the applications of Support Vector Machine to classify each pixel whether it belongs to a vessel or not. Single mask filters which consist of intensity values of normalized green channel have been generated according to the direction of angles. These single oriented mask filters contain the vectors of the neighbourhood of each pixel. Five images randomly selected from DRIVE database are used to train the classifier. Every single oriented mask filters are ranked according to the average accuracy of training images and their weights are assigned based on this rank. Ensemble approaches that are Addition With Weight and Product With Weight have been used to combine all these single mask filters. In order to test the proposed approach, two standard databases, DRIVE and STARE have been used. The results of the proposed method clearly show improvement compared to other single oriented mask filters.

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Section: Retinal Blood Vessel Segmentation Using Ensemble Of Single Omentioning

confidence: 99%

“…Pixels under study that are not categorized as vessel are reprocess for further investigation. In a work done by [6], a multiscale line detection is used to segment retinal blood vessel. The concept of changing the length of a basic line detector have been used in their work.…”

Section: Related Workmentioning

confidence: 99%

Retinal Blood Vessel Segmentation Using Ensemble of Single Oriented Mask Filters

Kasmin

Abdullah

Prabuwono

2017

IJECE

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“…This database was made by Department of Computing and Informatics, University of Lincoln, Lincoln, UK in 2008 [14].…”

Section: Review Databasementioning

confidence: 99%

“…Nguyen et al [14] used multiscale line detector method for blood vessel segmentation and Moghimirad et al [43] proposed multiscale method for retinal blood vessel segmentation based on weighted twodimensional (2D) medialness function.…”

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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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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“…For any pixel position, if the gray scale value in the edge image is 255 (white or edge pixel) then we find the pixel (x2, y2) in the opposite edge(mirror of this pixel) considering θ = 0 + 180 and varying r this can be described in [17], [18]. After applying this operation we obtain the pairs of pixels which are on the opposite edges (at line end points) giving imaginary lines passing through the centerline pixels.…”

Section: ଶmentioning

confidence: 99%

Detection of Blood Vessels and Measurement of Vessel width for Diabetic Retinopathy

Sukanya¹,

Abinaya²,

Tamilselvi³

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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An effective retinal blood vessel segmentation method using multi-scale line detection

Cited by 449 publications

References 31 publications

Retinal Blood Vessel Segmentation Using Ensemble of Single Oriented Mask Filters

Retinal Blood Vessel Segmentation Using Ensemble of Single Oriented Mask Filters

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

Detection of Blood Vessels and Measurement of Vessel width for Diabetic Retinopathy

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