Irregular motion recovery in fluorescein angiograms

Domingo, Juan Carlos Ureta; Ayala, Guillermo; Simó, Amelia; Ves, Esther de; Martínez-Costa, L.; Marco, P.

doi:10.1016/s0167-8655(97)00081-0

Cited by 14 publications

(2 citation statements)

References 16 publications

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“…To address these problems, several algorithms with varied levels of success have been proposed through the years. 21,[43][44][45][46][47][48][49][50][51] In our method, to accurately register relatively low-quality clinical FA images, we utilize a two-step nonrigid registration approach: a robust global vessel-based registration method based on the RANdom SAmpling & Consequence (RANSAC) algorithm, 52 followed by a more accurate nonrigid intensity multiresolution registration of FA images.…”

Section: Below)mentioning

confidence: 99%

Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular Edema

Rabbani

Allingham

Mettu

et al. 2015

Investigative Ophthalmology & Visual Science

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Section: Below)mentioning

confidence: 99%

Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular Edema

Rabbani

Allingham

Mettu

et al. 2015

Investigative Ophthalmology & Visual Science

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“…And, automated or computer-assisted analysis of diabetic patients' retina can help eye care specialist to screen larger populations of patients. In [2], DR was evaluated through Fluorescien Angiograms (FA). FA is a common medical estimation tool for screening DR.…”

Section: Introductionmentioning

confidence: 99%

Screening Diabetic Retinopathy Through Color Retinal Images

Jin

Gao

et al. 2007

Lecture Notes in Computer Science

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Abstract. Diabetic Retinopathy (DR) is a common complication of diabetes that damages the eye's retina. Recognition DR as early as possible is very important to protect patients' vision. We propose a method for screening DR and distinguishing Prolifetive Diabetic Retinopathy (PDR) from Non-Prolifetive Retinopathy (NPDR) automatatically through color retinal images. This method evaluates the severity of DR by analyzing the appearnce of bright lesions and retinal vessel patterns. The bright lesions are extracted through morphlogical reconsturction. After that, the retinal vessels are automatically extracted using multiscale matched filters. Then the vessel patterns are analyzed by extracting the vessel net density. The experimental results domonstrate that it is a effective solution to screen DR and distinguish PDR from NPDR by only using color retinal images.

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