An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image

Xu, Xiayu; Ding, Wenxiang; Abrmoff, Michael D.; Cao, Ruofan

doi:10.1016/j.cmpb.2017.01.007

Cited by 86 publications

(45 citation statements)

References 22 publications

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“…Table 7 presents the reported results of other a/v classification methods. Impressive results by Estrada [21] and Xu [18] were reported, both achieved classification accuracies of +90%. The results reported by Kondermann [11] have been considerably boosted using manual vessel segmentation, use of automatic segmentation sees the results deteriorate by 10%.…”

Section: Discussionmentioning

confidence: 99%

“…The results reported by Kondermann [11] have been considerably boosted using manual vessel segmentation, use of automatic segmentation sees the results deteriorate by 10%. Amongst the datasets used by these methods [18,21] dataset. Finally, the possibility of deep learning completely taking over the whole procedure is also viable in the future, directly performing segmentation of the background, arterioles, venules and the optic disc.…”

Section: Discussionmentioning

confidence: 99%

“…Performance was improved using the prior knowledge that arterioles and venules usually come in pairs, thus an iterative approach was used to match arteriole venule pairs. Xu [18] proposed novel features for a/v classification which included first and second order texture features measured from the vessel profile and an image patch around the target pixel. A k-nearest neighbour was used for classification.…”

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confidence: 99%

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Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort

Welikala

Foster

Whincup

et al. 2017

Computers in Biology and Medicine

106

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The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these associations. This requires automated systems that extract quantitative measures of vessel morphology from large numbers of retinal images. Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules. Therefore, the accurate detection of the vessel type is an important element in such automated systems. This paper presents a deep learning approach for the automatic classification of arterioles and venules across the entire retinal image, including vessels located at the optic disc. This comprises of a convolutional neural network whose architecture contains six learned layers: three convolutional and three fully-connected. Complex patterns are automatically learnt from the data, which avoids the use of hand crafted features. The method is developed and evaluated using 835,914 centreline pixels derived from 100 retinal images selected from the 135,867 retinal images obtained at the UK Biobank (large population-based cohort study of middle aged and older adults) baseline examination. This is a challenging dataset in respect to image quality and hence arteriole/venule classification is required to be highly robust. The method achieves a significant increase in accuracy of 8.1% when compared to the baseline method, resulting in an arteriole/venule classification accuracy of 86.97% (per pixel basis) over the entire retinal image.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort

Welikala

Foster

Whincup

et al. 2017

Computers in Biology and Medicine

106

View full text Add to dashboard Cite

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“…Furthermore, a single mislabel along the propagation may lead to mislabel of the entire vessel tree. On the other hand, the pixel classification methods extract hand-crafted local features around pixels with known true labels and build classifiers or likelihood models for test images [8][9][10][11][12][13]. The local features are usually designed based on observable colorimetric and geometric differences between arterioles and venules.…”

Section: Simultaneous Arteriole and Venule Segmentation With Domain-smentioning

confidence: 99%

“…Vazquez et al proposed a retinex image enhancement method to adjust the uneven illumination inside a retinal image [12]. Our group also proposed an intra-image and interimage normalization method to reduce the sample differences in feature space [13]. Pixel classification methods often struggle at finding the most representative features.…”

Section: Simultaneous Arteriole and Venule Segmentation With Domain-smentioning

confidence: 99%

Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database

Wang

et al. 2018

Biomed. Opt. Express

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The segmentation and classification of retinal arterioles and venules play an important role in the diagnosis of various eye diseases and systemic diseases. The major challenges include complicated vessel structure, inhomogeneous illumination, and large background variation across subjects. In this study, we employ a fully convolutional network to simultaneously segment arterioles and venules directly from the retinal image, rather than using a vessel segmentation-arteriovenous classification strategy as reported in most literature. To simultaneously segment retinal arterioles and venules, we configured the fully convolutional network to allow true color image as input and multiple labels as output. A domain-specific loss function was designed to improve the overall performance. The proposed method was assessed extensively on public data sets and compared with the state-of-the-art methods in literature. The sensitivity and specificity of overall vessel segmentation on DRIVE is 0.944 and 0.955 with a misclassification rate of 10.3% and 9.6% for arteriole and venule, respectively. The proposed method outperformed the state-of-the-art methods and avoided possible error-propagation as in the segmentation-classification strategy. The proposed method was further validated on a new database consisting of retinal images of different qualities and diseases. The proposed method holds great potential for the diagnostics and screening of various eye diseases and systemic diseases.

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Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

Jayachandran,

Ratheesh Kumar

2023

Int J Imaging Syst Tech

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Microaneurysms (MAs) are the early indications of diabetic retinopathy (DR), which may result in total visual loss. MAs detection is an exciting work due to its small, darkish color and subtle nature. The automatic detection and categorization of MAs in retinal fundus images using a multi‐scale approach based on Deep Neural Networks and Neighborhood Analysis is proposed in this research article. MAs segmentation, classification, and preprocessing comprise the three steps of the proposed technique. To extract the multi‐scale MA features, the auto‐weight dilated convolutional unit (AD) is specifically used for convolutional feature maps. To fuse convolutional feature maps in encoding layers, the AD unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is incorporated into the AD unit. We integrated the AD unit with the benefits of the U‐Net network for deep and shallow features. Additionally, in order to improve the suggested model and produce the final derivation, we developed a novel optimization approach. After that, neighborhood analysis is performed to name the Micro‐aneurysm because the lesion is actually a collection of independent little images rather than the entire image. The classification accuracy of the proposed method for the three different data sets such as MESSIDOR, online retinal fundus image database for glaucoma analysis (ORIGA), and RIM‐ONE‐R1, is 99.28%, 98.95%, and 98.76% respectively. The results show a good performance of the proposed model against the other analyzed procedure.

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An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image

Cited by 86 publications

References 22 publications

Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort

Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort

Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database

Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

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