Zero-crossing interval correction in tracing eye-fundus blood vessels

Tamura, Shinichi; Okamoto, Yoshiharu; Yanashima, Kenji

doi:10.1016/0031-3203(88)90057-x

Cited by 95 publications

(41 citation statements)

References 3 publications

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“…This approach has shown to work well, if there are no or only few pathologies like exudates, that also appear very bright and are also well contrasted. In [6] an area threshold is used to localize the optic disc. The contours are detected by means of the Hough transform.…”

Section: State Of the Artmentioning

confidence: 99%

Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques

Walter

Klein

2001

Medical Data Analysis

171

101

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Abstract. This paper presents new algorithms based on mathematical morphology for the detection of the optic disc and the vascular tree in noisy low contrast color fundus photographs. Both features -vessels and optic disc -deliver landmarks for image registration and are indispensable to the understanding of retinal fundus images. For the detection of the optic disc, we first find the position approximately. Then we find the exact contours by means of the watershed transformation. The algorithm for vessel detection consists in contrast enhancement, application of the morphological top-hat-transform and a post-filtering step in order to distinguish the vessels from other blood containing features.

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Section: State Of the Artmentioning

confidence: 99%

Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques

Walter

Klein

2001

Medical Data Analysis

171

101

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“…5, the vessel of interest has two overcrosses with another vessel and two bifurcations of itself. In this case, the proposed algorithm can identify the path correctly via graph search while other methods (e.g., the probing-based methods described in [11,12]) cannot find the right path or provide a wrong output as they only consider the local information (e.g., the centerline direction at the current path node, the change of the local vessel width, the consistency of the local centerline direction, etc.) for path probing.…”

Section: A*-based Path Searchmentioning

confidence: 99%

“…In [11], Hough transform is first used to locate a start point for the inspected vessel in a retinal image. Then, vessel tracing operation is started from the selected start point.…”

Section: Introductionmentioning

confidence: 99%

Retinal Vessel Detection and Measurement for Computer-aided Medical Diagnosis

Wee

2013

J Digit Imaging

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Since blood vessel detection and characteristic measurement for ocular retinal images is a fundamental problem in computer-aided medical diagnosis, automated algorithms/systems for vessel detection and measurement are always demanded. To support computer-aided diagnosis, an integrated approach/solution for vessel detection and diameter measurement is presented and validated. In the proposed approach, a Dempster-Shafer (D-S)-based edge detector is developed to obtain initial vessel edge information and an accurate vascular map for a retinal image. Then, the appropriate path and the centerline of a vessel of interest are identified automatically through graph search. Once the vessel path has been identified, the diameter of the vessel will be measured accordingly by the algorithm in real time. To achieve more accurate edge detection and diameter measurement, mixed Gaussian-matched filters are designed to refine the initial detection and measures. Other important medical indices of retinal vessels can also be calculated accordingly based on detection and measurement results. The efficiency of the proposed algorithm was validated by the retinal images obtained from different public databases. Experimental results show that the vessel detection rate of the algorithm is 100 % for large vessels and 89.9 % for small vessels, and the error rate on vessel diameter measurement is less than 5 %, which are all well within the acceptable range of deviation among the human graders.

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“…In [2], an area threshold is used to localize the optic disc. The contours are detected by means of the Hough transform, i.e., the gradient of the image is calculated, and the best fitting circle is determined.…”

Section: Figure1 Various Features Of a Typical Retinopathy Imagementioning

confidence: 99%

Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques

Singh¹,

Tripathi²

2010

IJCA

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Diabetic retinopathy (DR) is a common retinal complication associated with diabetes. It is a major cause of blindness in middle as well as older age groups. Therefore early detection through regular screening and timely intervention will be highly beneficial in effectively controlling the progress of the disease. Since the ratio of people afflicted with the disease to the number of eye specialist who can screen these patients is very high, there is a need of automated diagnostic system for diabetic retinopathy changes in the eye so that only diseased persons can be referred to the specialist for further intervention and treatment.Various aspects and stages of retinopathy are analyzed by examining the colored retinal images. Microaneurysms are small saccular pouches caused by local distension of capillary walls and appear as small red dots. Their walls are thin and rupture easily to cause hemorrhages. Hard exudates are yellow lipid deposits which appear as bright yellow lesions. The bright circular region from where the blood vessels emanate is called the optic disk. The fovea defines the center of the retina, and is the region of highest visual acuity. The spatial distribution of exudates and microaneurysms and hemorrhages, especially in relation to the fovea can be used to determine the severity of diabetic retinopathy.Image analysis tools can be used for automated detection of these various features and stages of Diabetes Retinopathy and can be referred to the specialist accordingly for intervention, thus making it a very effective tool for effective screening of Diabetic Retinopathy patients. DR patients require frequent, at least six monthly screening of vast number of patients and automating the process will go a long way in relieving the burden on the specialist and reducing the most common cause of preventable blindness.

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Zero-crossing interval correction in tracing eye-fundus blood vessels

Cited by 95 publications

References 3 publications

Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques

Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques

Retinal Vessel Detection and Measurement for Computer-aided Medical Diagnosis

Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques

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