An effective retinal blood vessel segmentation method using multi-scale line detection
TL;DR: The proposed method for automatically extracting blood vessels from colour retinal images is based on the fact that by changing the length of a basic line detector, line detectors at varying scales are achieved and it produces accurate segmentation on central reflex vessels while keeping close vessels well separated.
About: This article is published in Pattern Recognition.The article was published on 2013-03-01. It has received 434 citations till now. The article focuses on the topics: Segmentation.
Citations
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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 background or methods from "An effective retinal blood vessel s..."
...[42] and responses to 2-D Gabor wavelets [7] are used to compute the unary potentials, and a vessel-enhanced image processed with the method by Zana and Klein [22] for the pairwise potentials....
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...Parameters for computing the unary features were initially fixed to the values reported by the original references, which use the DRIVE training set [7], [42]....
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...The authors would like to thank the authors of [7], [36], [40], and [42] for providing them with their code, and [15] and [24] for providing them with their segmentations....
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...The angles were set to the values reported in the original references [7], [22], [42]....
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17 Oct 2016
TL;DR: This paper formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture based on a multi-scale and multi-level Convolutional Neural Network with a side-output layer to learn a rich hierarchical representation.
Abstract: Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE_DB1 datasets with an efficient running time.
421 citations
Cites methods from "An effective retinal blood vessel s..."
...utilized a multi-scale line detection scheme to compute vessel segmentation [12]....
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17 Oct 2016
TL;DR: Deep Retinal Image Understanding is presented, a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation and shows super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.
Abstract: This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus images. DRIU uses a base network architecture on which two set of specialized layers are trained to solve both the retinal vessel and optic disc segmentation. We present experimental validation, both qualitative and quantitative, in four public datasets for these tasks. In all of them, DRIU presents super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.
416 citations
Cites methods from "An effective retinal blood vessel s..."
...Classic methods for addressing the task of blood vessel segmentation involve hand crafted filters like line detectors [17,14] and vessel enhancement techniques [20,26,5]....
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TL;DR: The proposed infinite active contour model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map, and outperforms its competitors when compared with other widely used unsupervised and supervised methods.
Abstract: Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using ${\cal L}^{2}$ Lebesgue measure of the $\gamma$ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., ${\cal H}^{1}$ Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.
319 citations
Cites methods from "An effective retinal blood vessel s..."
...We selected nine unsupervised segmentation methods [5], [6], [11], [12], [15], [41]–[43]....
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13 Apr 2016
TL;DR: This paper formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map that distinguishes the vessels and background in the inadequate contrast region and has robustness to the pathological regions in the fundus image.
Abstract: Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.
263 citations
Cites methods from "An effective retinal blood vessel s..."
...[3] utilized a multi-scale line detection to produce the final vessel segmentation....
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References
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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.
Abstract: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.
3,416 citations
"An effective retinal blood vessel s..." refers background or methods in this paper
...In the combination process, we assign the same weight for each scale and the final segmentation is the linear combination of line responses of different scales....
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...A complete review of existing methods for retinal blood vessel segmentation can be referenced at [9]....
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...The early detection of these changes is extremely important in order to perform early intervention and prevent the patients from major vision loss....
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...For example, it has been shown that W¼15 is a good choice for retinal images in DRIVE dataset [6] where the typical vessel width is 7–8 pixels [8]....
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...Furthermore, these methods often require the ‘re-training’ process when performing on the new set of images to achieve optimal performance....
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TL;DR: An automated method to locate and outline blood vessels in images of the ocular fundus that uses local and global vessel features cooperatively to segment the vessel network is described.
Abstract: Describes an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. The authors' method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. The authors evaluate their method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that the authors' method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, they also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over the authors' method. They are making all their images and hand labelings publicly available for interested researchers to use in evaluating related methods.
2,206 citations
"An effective retinal blood vessel s..." refers background or methods in this paper
...A limitation of matched filter based methods is its naive assumption that the cross-sectional intensity profile of a vessel follows the shape of a Gaussian, which is not always the case (for example, in the presence of central reflex)....
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...In the combination process, we assign the same weight for each scale and the final segmentation is the linear combination of line responses of different scales....
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TL;DR: The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images and the results are compared to those obtained with other methods.
Abstract: Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods. >
1,692 citations
"An effective retinal blood vessel s..." refers background in this paper
...Different variants [17–20] have been proposed to improve the performance of the original matched filter response, ranging from the use of a threshold probing technique [17], ‘double-sided’ thresholding [19], or the first order derivative of Gaussian image [20] to provide a better thresholding…...
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TL;DR: In this paper, a method for automated segmentation of the vasculature in retinal images is presented, which produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector.
Abstract: We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods
1,435 citations
TL;DR: A neural network scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation that is suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
Abstract: This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
913 citations