Robust Vessel Segmentation in Fundus Images
12 Dec 2013-International Journal of Biomedical Imaging (Hindawi Publishing Corporation)-Vol. 2013, pp 154860-154860
TL;DR: A method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method is presented and a new high resolution fundus database is proposed to compare it to the state-of-the-art algorithms.
Abstract: One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.
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TL;DR: A review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.
1,053 citations
TL;DR: Wang et al. as discussed by the authors proposed Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end-to-end manner for retinal vessel segmentation.
Abstract: Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.
448 citations
TL;DR: Results suggest that this method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
Abstract: Goal: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model. Methods: Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Results: Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available datasets: DRIVE, STARE, CHASEDB1, and HRF. Additionally, a quantitative comparison with respect to other strategies is included. Conclusion: The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean, and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood-based approach. Significance: Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
429 citations
Cites methods from "Robust Vessel Segmentation in Fundu..."
...To the best of our knowledge, only unsupervised methods were previously tested on HRF [24], [55]....
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TL;DR: A new segment-level loss which emphasizes more on the thickness consistency of thin vessels in the training process is proposed which can bring consistent performance improvement for both deep and shallow network architectures.
Abstract: Objective: Deep learning based methods for retinal vessel segmentation are usually trained based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to-pixel matching between a predicted probability map and the corresponding manually annotated segmentation. However, due to the highly imbalanced pixel ratio between thick and thin vessels in fundus images, a pixel-wise loss would limit deep learning models to learn features for accurate segmentation of thin vessels, which is an important task for clinical diagnosis of eye-related diseases. Methods: In this paper, we propose a new segment-level loss which emphasizes more on the thickness consistency of thin vessels in the training process. By jointly adopting both the segment-level and the pixel-wise losses, the importance between thick and thin vessels in the loss calculation would be more balanced. As a result, more effective features can be learned for vessel segmentation without increasing the overall model complexity. Results: Experimental results on public data sets demonstrate that the model trained by the joint losses outperforms the current state-of-the-art methods in both separate-training and cross-training evaluations. Conclusion: Compared to the pixel-wise loss, utilizing the proposed joint-loss framework is able to learn more distinguishable features for vessel segmentation. In addition, the segment-level loss can bring consistent performance improvement for both deep and shallow network architectures. Significance: The findings from this study of using joint losses can be applied to other deep learning models for performance improvement without significantly changing the network architectures.
317 citations
Cites methods from "Robust Vessel Segmentation in Fundu..."
...Four public datasets DRIVE [23], STARE [38], CHASE_ DB1 [39] and HRF [40] are used to evaluate the proposed...
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...Finally, we apply the jointloss deep learning framework on the high-resolution fundus dataset HRF. Exemplar results1 are shown in the following sections....
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...Four public datasets DRIVE [23], STARE [38], CHASE_ DB1 [39] and HRF [40] are used to evaluate the proposed segment-level loss and the joint-loss framework....
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...HRF contains 15 images of healthy patients, 15 images of patients with diabetic retinopathy and 15 images of glaucomatous patients with a 60° FOV and all images have the same resolution of 3504 × 2336 pixels....
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...After the data augmentation process, the numbers of training patches for the datasets DRIVE, STARE, CHASE_DB1, and HRF are 7869, 8677, 4789 and 4717 respectively....
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TL;DR: The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods.
Abstract: This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels . In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.
280 citations
Cites background or methods from "Robust Vessel Segmentation in Fundu..."
...For images from DRIVE and STARE and CHASE_DB1 datasets, “p = 0.2,” which ensures the red regions to be highlighted in the vessel enhanced binary images....
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...However, increasing the segmentation accuracy above 92% for abnormal retinal images with bright lesions (exudates and cotton wool spots), or red lesions (hemorrhages and microaneurysms), or variations in retinal illumination and contrast, while maintaining low computational complexity is a…...
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28,073 citations
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8,738 citations
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11 Oct 1998
TL;DR: The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter and a vesselness measure is obtained.
Abstract: The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter. A vesselness measure is obtained on the basis of all eigenvalues of the Hessian. This measure is tested on two dimensional DSA and three dimensional aortoiliac and cerebral MRA data. Its clinical utility is shown by the simultaneous noise and background suppression and vessel enhancement in maximum intensity projections and volumetric displays.
3,928 citations
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TL;DR: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina based on extraction of image ridges, which coincide approximately with vessel centerlines, which is compared with two recently published rule-based methods.
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3,416 citations