Measuring Retinal Vessel Tortuosity in 10-Year-Old Children: Validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) Program

Owen, Christopher G.; Rudnicka, Alicja R.; Müllen, Robert J.; Barman, Sarah; Monekosso, Dorothy; Whincup, Peter H.; Ng, Jeffrey; Paterson, Carl

doi:10.1167/iovs.08-3018

Cited by 368 publications

(205 citation statements)

References 34 publications

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“…The CHASE_DB1 dataset contains 28 images which are captured at 30 degree FOV with a resolution of 1280 × 960 pixels from 14 patients in Child Heart And Health Study in England [32].…”

Section: Methodsmentioning

confidence: 99%

“…One of the most important benefits of ST is the coherence concept which is calculated via its Eigenvalues. ST has been used for low-level feature analysis such as feature extraction, edge and corner detection after it was introduced by Förstner as well as Harris and Stevens [32,35,36]. In the literature, there are basically linear and nonlinear types of ST [37,38].…”

Section: Structure Tensormentioning

confidence: 99%

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Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Nergiz

Akın

2017

Symmetry

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Abstract:Retinal vessel segmentation is one of the preliminary tasks for developing diagnosis software systems related to various retinal diseases. In this study, a fully automated vessel segmentation system is proposed. Firstly, the vessels are enhanced using a Frangi Filter. Afterwards, Structure Tensor is applied to the response of the Frangi Filter and a 4-D tensor field is obtained. After decomposing the Eigenvalues of the tensor field, the anisotropy between the principal Eigenvalues are enhanced exponentially. Furthermore, this 4-D tensor field is converted to the 3-D space which is composed of energy, anisotropy and orientation and then a Contrast Limited Adaptive Histogram Equalization algorithm is applied to the energy space. Later, the obtained energy space is multiplied by the enhanced mean surface curvature of itself and the modified 3-D space is converted back to the 4-D tensor field. Lastly, the vessel segmentation is performed by using Otsu algorithm and tensor coloring method which is inspired by the ellipsoid tensor visualization technique. Finally, some post-processing techniques are applied to the segmentation result. In this study, the proposed method achieved mean sensitivity of 0.8123, 0.8126, 0.7246 and mean specificity of 0.9342, 0.9442, 0.9453 as well as mean accuracy of 0.9183, 0.9442, 0.9236 for DRIVE, STARE and CHASE_DB1 datasets, respectively. The mean execution time of this study is 6.104, 6.4525 and 18.8370 s for the aforementioned three datasets respectively.

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“…The CHASE_DB1 dataset contains 28 images which are captured at 30 degree FOV with a resolution of 1280 × 960 pixels from 14 patients in Child Heart And Health Study in England [32].…”

Section: Methodsmentioning

confidence: 99%

Section: Structure Tensormentioning

confidence: 99%

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Nergiz

Akın

2017

Symmetry

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“…The CHASEDB1 dataset 40 consists of 28 color retinal fundus images with sizes of 999 × 960 pixels, acquired from both the left and right eyes of 14 child subjects enrolled in the program Child Heart And Health Study in England. The data set contains two groups of manually segmented images provided by two observers.…”

Section: Databasesmentioning

confidence: 99%

Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

et al. 2017

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Abstract. Extraction of blood vessels in retinal images is an important step for computer-aided diagnosis of ophthalmic pathologies. We propose an approach for blood vessel tracking and diameter estimation. We hypothesize that the curvature and the diameter of blood vessels are Gaussian processes (GPs). Local Radon transform, which is robust against noise, is subsequently used to compute the features and train the GPs. By learning the kernelized covariance matrix from training data, vessel direction and its diameter are estimated. In order to detect bifurcations, multiple GPs are used and the difference between their corresponding predicted directions is quantified. The combination of Radon features and GP results in a good performance in the presence of noise. The proposed method successfully deals with typically difficult cases such as bifurcations and central arterial reflex, and also tracks thin vessels with high accuracy. Experiments are conducted on the publicly available DRIVE, STARE, CHASEDB1, and high-resolution fundus databases evaluating sensitivity, specificity, and Matthew's correlation coefficient (MCC). Experimental results on these datasets show that the proposed method reaches an average sensitivity of 75.67%, specificity of 97.46%, and MCC of 72.18% which is comparable to the state-of-the-art.

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“…We use four data sets to evaluate the proposed approach: DRIVE ), STARE (Hoover et al 2000), CHASE DB1 (Owen et al 2009) and HRF (Odstrcilik et al 2013). …”

Section: Data Setsmentioning

confidence: 99%

Unsupervised delineation of the vessel tree in retinal fundus images

Strisciuglio¹,

Vento²,

Azzopardi³

et al. 2015

Computational Vision and Medical Image Processing V

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Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding. We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods.

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Measuring Retinal Vessel Tortuosity in 10-Year-Old Children: Validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) Program

Cited by 368 publications

References 34 publications

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

Unsupervised delineation of the vessel tree in retinal fundus images

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