Literature DB >> 9790742

Macular edema computer-aided evaluation in ocular vein occlusions.

L Martínez-Costa1, P Marco, G Ayala, E de Ves, J Domingo, A Simó.   

Abstract

This paper is concerned with the use of digital fundus imaging to detect, quantify, and follow up macular angiographic leakage due to retinal vein occlusions. Images were matched automatically. We detected those pixels with a high increment in gray level within the closest area to the foveal center. Binary images displaying leakage were obtained. The procedure was checked against two observers' agreement. Twenty-one angiographic studies were collected. Two images of each sequence were selected for digitalization. Numerical descriptors of the leakage were proposed and quantification plots were designed for each pair of images. Interobserver concordance ranged between 82 and 98% when manually detected leakage was compared with computer segmented areas. The detection and quantification of leakage areas may serve as a guide for severity evaluation and treatment planning. Moreover, they permit a precise follow-up of macular edema. Copyright 1998 Academic Press.

Entities:  

Mesh:

Year:  1998        PMID: 9790742     DOI: 10.1006/cbmr.1998.1487

Source DB:  PubMed          Journal:  Comput Biomed Res        ISSN: 0010-4809


  3 in total

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Journal:  Invest Ophthalmol Vis Sci       Date:  2015-01-29       Impact factor: 4.799

2.  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

3.  Research on the Segmentation of Biomarker for Chronic Central Serous Chorioretinopathy Based on Multimodal Fundus Image.

Authors:  Jianguo Xu; Jianxin Shen; Qin Jiang; Cheng Wan; Zhipeng Yan; Weihua Yang
Journal:  Dis Markers       Date:  2021-09-03       Impact factor: 3.434

  3 in total

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