Blood Vessel Segmentation in Pathological Retinal Image

Han, Zhe; Yin, Yilong; Meng, Xianjing; Yang, Gongping; Yan, Xiaowei

doi:10.1109/icdmw.2014.16

Cited by 18 publications

(15 citation statements)

References 22 publications

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“…Each method has advantages and disadvantages, which depend s o n the condition and needs of researchers. However, from each method in this review we suggest to use morphological method by Singh et al [62] and multiscale method by Han et al [48] because the method show the good accuracy in segmentation which use STARE and DRIVE database. …”

Section: Resultsmentioning

confidence: 99%

“…This method has great visual presence, which good for describe shape and structure of retinal blood vessel image. The problem of this method is long time for training dan not detect thin vessel which caused low contrast image [48]. The future work to get higher performance of segmentation is to develop algorithm for removing optic disk and classify vessel into arteries and veins to detect diabetic retinopathy and to resolve the thin vessel problem can be done by combine with other method that can present thin vessel.…”

Section: Discussionmentioning

confidence: 99%

“…For Some of segmentation method of retinal blood vessel implementation and it's accuracy on retinal fundus image database. Based on segmentation method that described in this review, the highest accuracy method result is improved morphological method by Singh et al [62] and improved multiscale method by Han et al [48]. These methods compared DRIVE and STARE database.…”

Section: Discussionmentioning

confidence: 99%

“…Han et al [48] designed multiscale local statistical features basen on gray level and morphological fatures combined with features based local area shape. This method has great visual presence, which good for describe shape and structure of retinal blood vessel image.…”

Section: Multiscalementioning

confidence: 99%

See 3 more Smart Citations

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: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Multiscalementioning

confidence: 99%

See 2 more Smart Citations

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

Aras¹,

Lestari²,

Nugroho³

et al. 2016

CST

View full text Add to dashboard Cite

show abstract

“…Also, You et al [12] exploited the radial projection and semi-supervised classification using SVM for vessel segmentation. A supervised method for retinal blood vessel segmentation is presented in [13]. They designed the feature vectors by combining local area and shape information with the multi-scale statistical features based on gray level values.…”

Section: Introductionmentioning

confidence: 99%