Literature DB >> 29181431

Neovascularization detection in diabetic retinopathy from fluorescein angiograms.

Benjamin Béouche-Hélias1, David Helbert2, Cynthia de Malézieu1, Nicolas Leveziel1, Christine Fernandez-Maloigne2.   

Abstract

Although a lot of work has been done on optical coherence tomography and color images in order to detect and quantify diseases such as diabetic retinopathy, exudates, or neovascularizations, none of them is able to evaluate the diffusion of the neovascularizations in retinas. Our work has been to develop a tool that is able to quantify a neovascularization and the fluorescein leakage during an angiography. The proposed method has been developed following a clinical trial protocol; images are taken by a Spectralis (Heidelberg Engineering). Detections are done using a supervised classification using specific features. Images and their detected neovascularizations are then spatially matched by an image registration. We compute the expansion speed of the liquid that we call diffusion index. This last one specifies the state of the disease, permits indication of the activity of neovascularizations, and allows a follow-up of patients. The method proposed in this paper has been built to be robust, even with laser impacts, to compute a diffusion index.

Entities:  

Keywords:  anti-VEGF; classification; diabetes; diabetic retinopathy; neovascularization

Year:  2017        PMID: 29181431      PMCID: PMC5689132          DOI: 10.1117/1.JMI.4.4.044503

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  11 in total

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2.  Automated identification of diabetic retinal exudates in digital colour images.

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4.  Salient feature region: a new method for retinal image registration.

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5.  Parameter-free optic disc detection.

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6.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction.

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Journal:  IEEE Trans Med Imaging       Date:  2006-09       Impact factor: 10.048

7.  Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation.

Authors:  P A Legg; P L Rosin; D Marshall; J E Morgan
Journal:  Comput Med Imaging Graph       Date:  2013-08-30       Impact factor: 4.790

8.  Exudate detection in color retinal images for mass screening of diabetic retinopathy.

Authors:  Xiwei Zhang; Guillaume Thibault; Etienne Decencière; Beatriz Marcotegui; Bruno Laÿ; Ronan Danno; Guy Cazuguel; Gwénolé Quellec; Mathieu Lamard; Pascale Massin; Agnès Chabouis; Zeynep Victor; Ali Erginay
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9.  Fully automated diabetic retinopathy screening using morphological component analysis.

Authors:  Elaheh Imani; Hamid-Reza Pourreza; Touka Banaee
Journal:  Comput Med Imaging Graph       Date:  2015-03-21       Impact factor: 4.790

10.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

Authors:  Meindert Niemeijer; Bram van Ginneken; Stephen R Russell; Maria S A Suttorp-Schulten; Michael D Abràmoff
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-05       Impact factor: 4.799

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  1 in total

1.  A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

Authors:  Wanyue Li; Wangyi Fang; Jing Wang; Yi He; Guohua Deng; Hong Ye; Zujun Hou; Yiwei Chen; Chunhui Jiang; Guohua Shi
Journal:  Transl Vis Sci Technol       Date:  2022-03-02       Impact factor: 3.283

  1 in total

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