Iterative variational mode decomposition based automated detection of glaucoma using fundus images

Shishir Maheshwari; Ram Bilas Pachori; Vivek Kanhangad; Sulatha V. Bhandary; U. Rajendra Acharya

doi:10.1016/j.compbiomed.2017.06.017

ScienceGate Book Chapters

JOURNAL ARTICLE

Iterative variational mode decomposition based automated detection of glaucoma using fundus images

Shishir Maheshwari Ram Bilas Pachori Vivek Kanhangad Sulatha V. Bhandary U. Rajendra Acharya

Year: 2017 Journal: Computers in Biology and Medicine Vol: 88 Pages: 142-149 Publisher: Elsevier BV

DOI: 10.1016/j.compbiomed.2017.06.017

Get Full-Text PDF Get Analytical Report

Keywords:

Glaucoma Artificial intelligence Computer science Support vector machine Pattern recognition (psychology) Fundus (uterus) Computer vision Entropy (arrow of time) Ophthalmology Medicine

Metrics

140

Cited By

10.95

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Retinal Imaging and Analysis

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Glaucoma and retinal disorders

Health Sciences → Medicine → Ophthalmology

Machine Learning in Bioinformatics

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Iterative variational mode decomposition based automated detection of glaucoma using fundus images

Metrics

Citation History

Topics

Related Documents

Automated Glaucoma Detection Using Variational Mode Decomposition from Fundus Images

Automated glaucoma detection using quasi‐bivariate variational mode decomposition from fundus images

Automated detection of glaucoma in fundus images using variational mode decomposition and textural features

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Glaucoma Detection using Bi-Dimensional Empirical Mode Decomposition from Retinal Fundus Images