Journal article
An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image
Computer Methods and Programs in Biomedicine, Vol.141, pp.3-9
04/2017
DOI: 10.1016/j.cmpb.2017.01.007
PMID: 28241966
Abstract
•This paper proposed an improved pixel-classification based method for artery and vein classification on retinal image.•Intra-image regularization and inter-subject normalization are first used to reduce the image differences in feature space.•Novel features, including first-order and second-order texture features, are introduced to capture the discriminating characteristics.•A high accuracy of 0.923 was achieved on a public database.•The proposed method holds great potential to serve as an early diagnostic tool for various diseases, such as diabetic retinopathy. Retinal artery and vein classification is an important task for the automatic computer-aided diagnosis of various eye diseases and systemic diseases. This paper presents an improved supervised artery and vein classification method in retinal image. Intra-image regularization and inter-subject normalization is applied to reduce the differences in feature space. Novel features, including first-order and second-order texture features, are utilized to capture the discriminating characteristics of arteries and veins. The proposed method was tested on the DRIVE dataset and achieved an overall accuracy of 0.923. This retinal artery and vein classification algorithm serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases.
Details
- Title: Subtitle
- An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image
- Creators
- Xiayu Xu - The Key Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. ChinaWenxiang Ding - The Key Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. ChinaMichael D Abràmoff - Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USARuofan Cao - The Key Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China
- Resource Type
- Journal article
- Publication Details
- Computer Methods and Programs in Biomedicine, Vol.141, pp.3-9
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.cmpb.2017.01.007
- PMID
- 28241966
- ISSN
- 0169-2607
- eISSN
- 1872-7565
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 81401480; DOI: 10.13039/501100002858, name: China Postdoctoral Science Foundation, award: 2014M552460, 2016T90929; name: International Science & Technology Cooperation Program of China, award: 2013DFG02930; name: National Instrumentation Program, award: 2013YQ190467
- Language
- English
- Date published
- 04/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Ophthalmology and Visual Sciences
- Record Identifier
- 9983806288002771
Metrics
36 Record Views