Automated glaucoma classification using advanced image decomposition techniques from retinal fundus images

Deepak Parashar; Dheraj Kumar Agrawal; Praveen Kumar Tyagi; Neha Rathore

doi:10.4018/978-1-6684-3947-0.ch013

Automated glaucoma classification using advanced image decomposition techniques from retinal fundus images

Deepak Parashar^*
, Dheraj Kumar Agrawal
, Praveen Kumar Tyagi
, Neha Rathore

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

2 Citations (Scopus)

Abstract

Glaucoma is one of the main reasons for invariant retinal cecity. Several approaches have been developed to screen glaucoma based on fundus photographs. This chapter investigated automated glaucoma classification methods using advanced image decomposition algorithms such as EWT, DWT, EMD, VMD, and FAWT. This study computed significant texture-based descriptors from the high-frequency descriptors followed by the LS-SVM classifier classification. The robustness of the developed CAD system has been tested using the RIM-ONE public database.

Original language	English
Title of host publication	AI-Enabled Smart Healthcare Using Biomedical Signals
Publisher	IGI Global
Pages	240-258
Number of pages	19
ISBN (Electronic)	9781668439487
ISBN (Print)	9781668439470
DOIs	https://doi.org/10.4018/978-1-6684-3947-0.ch013
Publication status	Published - 27-05-2022

All Science Journal Classification (ASJC) codes

General Engineering
General Biochemistry,Genetics and Molecular Biology
General Computer Science

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10.4018/978-1-6684-3947-0.ch013

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AB - Glaucoma is one of the main reasons for invariant retinal cecity. Several approaches have been developed to screen glaucoma based on fundus photographs. This chapter investigated automated glaucoma classification methods using advanced image decomposition algorithms such as EWT, DWT, EMD, VMD, and FAWT. This study computed significant texture-based descriptors from the high-frequency descriptors followed by the LS-SVM classifier classification. The robustness of the developed CAD system has been tested using the RIM-ONE public database.

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ER -

Automated glaucoma classification using advanced image decomposition techniques from retinal fundus images

Abstract

All Science Journal Classification (ASJC) codes

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