A novel method for retinal vessel tracking using particle filters

Nayebifar, B.; Moghaddam, Hamid Abrishami

doi:10.1016/j.compbiomed.2013.01.016

Cited by 31 publications

(20 citation statements)

References 8 publications

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“…Line-detector filters model the vessels as multiscale lines [21]; bifurcations can then be reconstructed by analyzing the polar response of the filter [22]. As a second step to reduce the false negatives, regiongrowing techniques are often used by aggregating pixels of similar intensities along the vessel orientations [18,15,23]. Classification-based segmentation methods are used either directly or to reduce false positives after applying edgebased techniques.…”

Section: Introductionmentioning

confidence: 99%

Joint segmentation and classification of retinal arteries/veins from fundus images

Girard

Kavalec

Cheriet

2019

Artificial Intelligence in Medicine

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Objective: Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods: A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree.Results: The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion: The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance: The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.

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Section: Introductionmentioning

confidence: 99%

Joint segmentation and classification of retinal arteries/veins from fundus images

Girard

Kavalec

Cheriet

2019

Artificial Intelligence in Medicine

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“…Zhao et al [11] applied level set and region growth to segment retinal blood vessel. Nayebifar et al [12] used particle filtering to track the retinal vessel paths for automatic blood vessel segmentation. The method can describe the structure of the vascular network comprehensively, and the adaptability is good, but the computation amount is large and depends on the selection of the initial seed point and direction.…”

Section: Unsupervised Methodsmentioning

confidence: 99%

BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

Guo

Peng

2020

BMC Med Imaging

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Background: Retinal blood vessel segmentation has an important guiding significance for the analysis and diagnosis of cardiovascular diseases such as hypertension and diabetes. But the traditional manual method of retinal blood vessel segmentation is not only time-consuming and laborious but also cannot guarantee the accuracy and efficiency of diagnosis. Therefore, it is especially significant to create a computer-aided method of automatic and accurate retinal vessel segmentation. Methods: In order to extract the blood vessels' contours of different diameters to realize fine segmentation of retinal vessels, we propose a Bidirectional Symmetric Cascade Network (BSCN) where each layer is supervised by vessel contour labels of specific diameter scale instead of using one general ground truth to train different network layers. In addition, to increase the multi-scale feature representation of retinal blood vessels, we propose the Dense Dilated Convolution Module (DDCM), which extracts retinal vessel features of different diameters by adjusting the dilation rate in the dilated convolution branches and generates two blood vessel contour prediction results by two directions respectively. All dense dilated convolution module outputs are fused to obtain the final vessel segmentation results. Results: We experimented the three datasets of DRIVE, STARE, HRF and CHASE_DB1, and the proposed method reaches accuracy of 0.9846/0.9872/0.9856/0.9889 and AUC of 0.9874/0.9941/0.9882/0.9874 on DRIVE, STARE, HRF and CHASE_DB1. Conclusions:The experimental results show that compared with the state-of-art methods, the proposed method has strong robustness, it not only avoids the adverse interference of the lesion background but also detects the tiny blood vessels at the intersection accurately.A new study published in the British "Lancet Global Health" predicts that if the treatment of eye diseases is not improved by better funding, the number of blind people worldwide will increase to 115 million by 2050, 2.2 times more than the current 36 million [1]. In fact, Retinal vessel disease in the fundus is one of the vital causes of blindness and many can be prevented in advance by fundus

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“…The segmentation of the fundus of the retina has long been the focus of experts around the world. There are many traditional state-of-the-art algorithms for segmenting the fundus of the eye, and these algorithms can be broadly divided into two categories: Unsupervised and supervised [5,6]. The image segmentation methods in medical imaging includes superpixel segmentation methods [7,8], watershed segmentation methods [9,10], and active contour methods [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation

et al. 2019

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Fundus image segmentation technology has always been an important tool in the medical imaging field. Recent studies have validated that deep learning techniques can effectively segment retinal anatomy and determine pathological structure in retinal fundus photographs. However, several groups of image segmentation methods used in medical imaging only provide a single retinopathic feature (e.g., roth spots and exudates). In this paper, we propose a more accurate and clinically oriented framework for the segmentation of fundus images from end-to-end input. We design a four-path multiscale input network structure that learns network features and finds overall characteristics via our network. Our network’s structure is not limited by segmentation of single retinopathic features. Our method is suitable for exudates, roth spots, blood vessels, and optic discs segmentation. The structure has general applicability to many fundus models; therefore, we use our own dataset for training. In cooperation with hospitals and board-certified ophthalmologists, the proposed framework is validated on retinal images from large databases and can improve diagnostic performance compared to state-of-the-art methods that use smaller databases for training. The proposed framework detects blood vessels with an accuracy of 0.927, which is comparable to exudate accuracy (0.939) and roth spot accuracy (0.904), providing ophthalmologists with a practical diagnostic and a robust analytical tool.

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A novel method for retinal vessel tracking using particle filters

Cited by 31 publications

References 8 publications

Joint segmentation and classification of retinal arteries/veins from fundus images

Joint segmentation and classification of retinal arteries/veins from fundus images

BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation

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