A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

Marín, Diego; Aquino, Arturo; Gegúndez-Arias, Manuel Emilio; Bravo, José Manuel

doi:10.1109/tmi.2010.2064333

Cited by 839 publications

(541 citation statements)

References 56 publications

(91 reference statements)

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“…Though, DR patients recognize no symptoms in the early stages, it shows in the later stages, until visual loss develops (Lee et al, 2001). As the treatment is insufficient we need an eye-fundus examination (Martın et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images

Dhiravidachelvi¹,

Rajamani²

2015

Journal of Computer Science

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Abstract:In recent years the medical profession has seen an ever increasing demand for diagnosis and a permanent cure for illnesses caused by climatic-changes, unwholesome food and environmental pollution. Also the appearances of hitherto unknown viral diseases have caused eye diseases, which have prompted surgeons to monitor the health of the eyes. Potential new therapies that may help in preserving sight in the growing population of diabetic patients into the 21st Century. Early detection of diseases affecting the eyes reduces the risk of permanent damage. Some of the serious conditions which warrant early diagnosis are: Glaucoma, floaters, macula degradation and diabetic retinopathy. In the early stages, a choice of treatment options exist, which dwindles as the disease spreads. A visual inspection of the optic disc, macula and the blood vessels of the eye requires to be done routinely. Diabetic patients run the risk of damage to retinal vessels, which are referred to as diabetic retinopathy. This may further be classified as: Non-proliferative diabetic retinopathy and proliferative diabetic retinopathy. In scientific literatures, feature extraction method has been reported for diagnosis and classification. In this study a systematic Decide, Detect, Determine and Do approach for analyzing diabetic retinopathy images has been taken up. The proposed approach gives a clearer picture of the abnormality, its type (NPDR or PDR), its status (viz., mild, moderate or severe) and finally the appropriate treatment.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images

Dhiravidachelvi¹,

Rajamani²

2015

Journal of Computer Science

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“…However, identification and quantification of such changes are challenging tasks since the retina is extremely heterogeneous and, as a consequence low signal-to-noise ratio, nonuniform illumination and contrast shifts in the images complicate the automated detection and analysis of geometrical changes. The latest segmentation algorithms produce highly accurate vessel segmentations [4] [5] [6] [7]. Nevertheless, the identification of the mutually overlapping venous and arterial trees is a nontrivial problem.…”

Section: Introductionmentioning

confidence: 99%

A fluid-dynamic based approach to reconnect the retinal vessels in fundus photography

Calivá

Hunter

Chudzik

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-This paper introduces the use of fluid-dynamic modeling to determine the connectivity of overlapping venous and arterial vessels in fundus images. Analysis of the retinal vascular network may provide information related to systemic and local disorders. However, the automated identification of the vascular trees in retinal images is a challenging task due to the low signal-to-noise ratio, nonuniform illumination and the fact that fundus photography is a projection on to the imaging plane of three-dimensional retinal tissue. A zero-dimensional model was created to estimate the hemodynamic status of candidate tree configurations. Simulated annealing was used to search for an optimal configuration. Experimental results indicate that simulated annealing was very efficient on test cases that range from small to medium size networks, while ineffective on large networks. Although for large networks the nonconvexity of the cost function and the large solution space made searching for the optimal solution difficult, the accuracy (average success rate = 98.35%), and simplicity of our novel approach demonstrate its potential effectiveness in segmenting retinal vascular trees.

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“…Examples of such approaches include machine learning, [3][4][5][6][7][8][9][10][11][12] matched filtering, [13,14] vessel tracking, [15,16] mathematical morphology, [17] model approaches, [18,19] and connected operators. [20,21] Machine-learning methods assign one or more groups to pixels in the retinal image, using multiple numeric pixel features to group them.…”

Section: Introductionmentioning

confidence: 99%

“…An extracted vessel tree is obtained using a tracking method. Marin et al [6] developed another supervised model for segmenting blood vessels. A nonlinear, multilayer feed forward neural network is used for training and classification, and their proposed model extracts grayscale values and moment-invariant features to represent each pixel in the retinal images.…”

Section: Introductionmentioning

confidence: 99%

Improved automated vessel segmentation for diagnosing eye diseases using fundus images

Onal¹,

Dabil-Karacal²

2015

JBGC

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The retina can provide evidence of diseases originating in other parts of the body. Among the eye diseases that can be diagnosed through a retinal examination, age-related macular degeneration, glaucoma, and diabetic retinopathy are the most common, and they cause vision loss. Although these diseases can be diagnosed by examining blood vessels and the optic disk in retinal images, assessment of blood vessels on colored fundus images is a time-consuming and subjective process. Here, we present an automated blood vessel segmentation algorithm that facilitates the evaluation of diabetic retinopathy through assessment of blood vessel abnormalities. The blood vessels are extracted using a random forest classification model combined with wavelet features and local binary pattern texture information. Discriminant analysis is modified and used for feature selection to train the proposed classification model. The boundary of the optic disk is identified using low-pass filtering, fuzzy c-means clustering, and template matching so that it may be removed and not confound the segmentation analysis. Validation test results using three publicly available retinal image datasets demonstrated that our proposed method achieves as good or better blood vessel segmentation accuracy than the other supervised model approaches examined. Results show that the proposed scheme is able to segment the blood vessels and optic disk structures accurately (Accuracy Index) in 95.80%, 95.20%, and 97.10% of the testing Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE), and CHASE_DB1 image datasets respectively. The main advantage of our proposed model is that it provides robust and computationally efficient segmentation of blood vessels and the optic disk. The proposed model aims to provide supportive information for cases in which a diagnosis remains unclear following a clinical examination.

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A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

Cited by 839 publications

References 56 publications

A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images

A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images

A fluid-dynamic based approach to reconnect the retinal vessels in fundus photography

Improved automated vessel segmentation for diagnosing eye diseases using fundus images

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