Retinal vessel segmentation using non-subsampled contourlet transform and multi-scale line detection

Masooomi, Razie; Ahmadifard, Alireza; Mohtadizadeh, Ali

doi:10.1109/iraniancis.2014.6802586

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Also, the proposed method is an unsupervised method, which requires no training data and thus can be implemented in real-time scenario. As [7] 0.94 0.95 Soares et al [8] 0.94 0.94 Ricci and perfetti [9] 0.95 0.95 Marin et al [11] 0.94 0.95 Fraz and Remagnin [5] 0.94 0.95 Roychowdhury et al [31] 0.95 0.95 Hoover et al [3] -0.92 Jiang and Mojon [32] 0.89 0.90 Mendonca and Campilho [33] 0.94 0.94 Lam and Yan [34] -0.94 Al-Diri et al [29] -0.96 Lam et al [35] 0.94 0.95 Budai et al [36] 0.95 0.93 Palomera-Perez et al [37] 0.92 0.92 Miri and Mahloojifar [22] 0.94 -Nguyen [30] 0.94 0.93 Razie Masooomi et al [38] 0.94 -Ricci and Perfetti [9] 0.94 0.94 Zana and Klein [19] 0.93 -Chauduri et al [15] 0.93 0.92 Martinez-Perez et al [26] 0 future scope, the proposed method may be extended for the detection of neovascularisation in diabetic retinopathy. [7] 900 Soares et al [8] 190 Lupascu et al [10] 125 Marin et al [11] 90 Ricci-SVM [9] 79 Ricci-line [9] 0.37 proposed method 3.3…”

Section: Resultsmentioning

confidence: 99%

Robust retinal blood vessel segmentation using line detectors with multiple masks

Biswal¹,

T²,

Subrahmanyam³

2018

IET Image Processing

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Section: Resultsmentioning

confidence: 99%

Robust retinal blood vessel segmentation using line detectors with multiple masks

Biswal¹,

T²,

Subrahmanyam³

2018

IET Image Processing

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“…Applying the CLAHE Fraz et al [21] Roychowdhury et al [22] Orlando and Blaschko [23] Wang et al [24] Moghimirad et al [25] Xiao et al [26] Imani et al [27] Roychowdhury et al [22] Zhang et al [28] Fraz et al [21] Zhao et al [29] Krause et al [30] Liu et al [31] Strisciuglio et al [32] Lázár and Hajdu. [33] Mendoça et al [34] Miri and Mahlooji [35] Fraz et al [36] Zhang et al [37] Perret and Collet [38] You et al [39] Yin et al [40] Argüello et al [41] Masooomi et al [42] Frangi et al [43] Kaba et al [44] Zhang et al [45] Li et al [46] Odstrčilík et al [18] Annunziata et al [47] Christodoulidis et al [48] Lázár and Hajdu. [33] Odstrčilík et al [18] Wang et al [24] Moghimirad et al [25] Imani et al [27] Liu et al [31] Annunziata et al [47] Strisciuglio et al [32] Fraz et al [21] Roychowdhury et al [22] Perret and Collet [38] Zhang et al [37] Zhao et al [29] Frangi et al...…”

Section: Performance Of the Algorithmsmentioning

confidence: 99%

Performance comparison of publicly available retinal blood vessel segmentation methods

Vostatek

Claridge

Uusitalo

et al. 2017

Computerized Medical Imaging and Graphics

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Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels. Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods by the linear regression model is tested for each database. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors. The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.

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“…The process of defining the features or characteristics of the image that most efficient or more meaningful, which represents important information for analysis and classification. This stage extracts features of pixels in image [58].…”

Section: Feature Extractionmentioning

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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Retinal vessel segmentation using non-subsampled contourlet transform and multi-scale line detection

Cited by 7 publications

References 19 publications

Robust retinal blood vessel segmentation using line detectors with multiple masks

Robust retinal blood vessel segmentation using line detectors with multiple masks

Performance comparison of publicly available retinal blood vessel segmentation methods

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

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