A Radon Transform Based Approach for Extraction of Blood Vessels in Conjunctival Images

Pourreza, Reza; Banaee, Touka; Pourreza, Hamidreza; Kakhki, Ramin Daneshvar

doi:10.1007/978-3-540-88636-5_89

Cited by 23 publications

(17 citation statements)

References 6 publications

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Section: Intruductionmentioning

confidence: 99%

“…However these methods have a high computation load since they apply various filters in all directions to detect all vessels. Radon transform based methods [17][18] or linear operators [19][20] also use linear segment features and intensity difference between vessels and surrounding ones.Another method for vessel segmentation is Edge-based algorithms which are the earliest segmentation approaches and still remain very popular. Edge-based segmentations rely on edges found in a retinal image by edge detection operators such as Canny operator [21][22], Sobel operator [11,23] and directional differential masks [25].…”

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confidence: 99%

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Vessel segmentation based on Sobel operator and fuzzy reasoning

Ghadiri

Akbarzadeh-T

Haddadan

2011

2011 1st International eConference on Computer and Knowledge Engineering (ICCKE)

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Vessel detection is a fundamental step in retinal analysis which helps to extract further information such as characterization of changes in blood vessels width and tortuosity. In this paper, we present an automatic algorithm based on edge detection and fuzzy inference. In the proposed method, the direction of linear structures are determined with Radon transform, then Sobel operator is used for extracting edges along with the predetermined direction. The combination of gradient information of vessel edges with fuzzy theory and genetic algorithm help us in the vessel validation process. Ultimately, the vessel is reconstructed via the extracted edges and morphological algorithms. Experimental results on 40 images of DRIVE database show that the proposed algorithm despite its simplicity has a high performance in comparison with other edge detector algorithms which are found to be sensitive to noise. Keywords-Sobel operator; Radon transform; Fuzzy inference; Genetic algorithm I. INTRUDUCTIONMany diseases such as Diabetes and hypertensive retinopathy can be diagnosed by investigating retinal images for various features of vessels such as width, length, color, and tortuosity [1]. A good vessel map can provide useful geometric information about the detection of other objects in retina images such as exude, macular and fovea. Thus, a reliable method of vessel detection and quantification would be valuable.Retinal vessels can be described as a dark object set against a lighter background with almost parallel edges. Vessels are piecewise linear structures and intensity profile of them is an approximation of a Gaussian or mixture Gaussian. These factors help us to detect vessels properly but there are some factors which make this process somehow difficult. Shape, size and the color of all vessels are not the same. Regarding these factors different techniques has been suggested for extracting vessels from retinal images.Vessel detection algorithms can be divided into several categories such as locally adaptive scheme methods, pattern recognition methods, tracking based methods, model based approaches and edge based methods. Among these various methods, pattern recognition methods and edge based methods are more important.Techniques based on vessel centerline fall into the category of pattern recognition. In these methods vessel centerline is detected by means of algorithms such as ridgebased algorithms [2][3][4][5], algorithms based on differential geometry [6,7] or thinning based algorithms [8][9][10].Linear segment detection methods are another prevalent method in pattern recognition category. These methods are based on the assumption that vessels are piecewise linear segments and profile of the vessel cross section can be approximated by a Gaussian-shape. Some match filters which properly describe these linear segments were designed and applied [11][12][13][14]. If a vessel width matches with the scale of filter, there will be a strong response. But they also respond to non-vessel samples such as Bright lesion and optic ...

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Section: Intruductionmentioning

confidence: 99%

mentioning

confidence: 99%

Vessel segmentation based on Sobel operator and fuzzy reasoning

Ghadiri

Akbarzadeh-T

Haddadan

2011

2011 1st International eConference on Computer and Knowledge Engineering (ICCKE)

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“…To alleviate this, Xu et al 19 combined Aylward's method with the recursive geometrical tracking approach proposed by Sun. 15 In contrast, Tavakoli et al 20 and Pourreza et al 21 proposed a tracking algorithm which uses Radon transform to compute local line integrals of the image, thus not relying on the Hessian. As a result their proposed method performs relatively well in the presence of noise.…”

Section: Introductionmentioning

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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“…An algorithm is proposed to extract blood vessels by classify vessels into some predefined class according to vessel width and compute for each class. The width examination algorithm is pointed out in [2,3], but the peaks detection method have not point out clearly. HAO Ying propose an improved algorithm for peak detection based on the basic principles of the Forstner operator in [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Radon Transform and AR Model in Crack Detection

Yang¹,

Xue²,

Ao³

et al. 2015

Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

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Abstract. Aiming at the peak extraction problem in the radon space, a peak extraction method based on AR model is proposed to detect component crack value. A profile analysis technique is used to take profile in Radon space and reconstruct profile with AR model to get a sharp and prominent peak for crack value extraction. Intensive experiments have shown that the proposed method is effective in estimating the component crack and lessening peak extraction difficulty.

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A Radon Transform Based Approach for Extraction of Blood Vessels in Conjunctival Images

Cited by 23 publications

References 6 publications

Vessel segmentation based on Sobel operator and fuzzy reasoning

Vessel segmentation based on Sobel operator and fuzzy reasoning

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

A Novel Radon Transform and AR Model in Crack Detection

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