An EMD-SVM screening system for retina digital images: The effect of kernels and parameters

Research output: Contribution to Book/Report typesContribution to conference proceedingspeer-review

1 Citation (Scopus)

Abstract

The discrete wavelet transform (DWT) and empirical mode decomposition (EMD) are employed to analyze retina digital images in the frequency domain. In particular, statistical features are extracted from high frequency components of the analyzed images. The purpose is to classify normal versus abnormal images. Three different pathologies are considered including, circinates, drusens, and microaneurysms (MA). Support vector machines (SVM) with polynomial and radial basis function kernel are used to classify retina digital images. The simulation results from leave-one-out method (LOOM) show the effectiveness of the EMD-based features over the DWT-based ones. In addition, the polynomial kernel performs better than the radial basis function kernel.

Original languageEnglish
Title of host publication2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012
Pages912-917
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 - Montreal, QC, Canada
Duration: 2 Jul 20125 Jul 2012

Publication series

Name2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012

Conference

Conference2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012
Country/TerritoryCanada
CityMontreal, QC
Period2/07/125/07/12

Fingerprint

Dive into the research topics of 'An EMD-SVM screening system for retina digital images: The effect of kernels and parameters'. These topics are generated from the title and abstract of the publication. Together, they form a unique fingerprint.

Cite this