Using curvelet transform to detect breast cancer in digital mammogram

Mohamed Meselhy M. Eltoukhy, Ibrahima Faye, Brahim Belhaouari Samir

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)

Abstract

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. The motivation of this approach is the desire of using the advantages of curvelet transform into mammogram analysis. Curvelet provide stable, efficient and near-optimal representation of otherwise smooth objects having discontinuities along smooth curves. Since medical images have several objects and curved shaped, it is expected that the curvelet transform would be better for classification of cancer classes in digital mammogram. To construct and evaluate a supervised classifier for this problem, by transforming the data of the images in curvelet basis and then using a special set of coefficients as the features tailored towards separating each of those classes. The experimental results indicate that using curvelet transform significantly improves the classification of cancer classes.

Original languageEnglish
Title of host publicationProceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
Pages340-345
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009 - Kuala Lumpur, Malaysia
Duration: 6 Mar 20098 Mar 2009

Publication series

NameProceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009

Conference

Conference2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
Country/TerritoryMalaysia
CityKuala Lumpur
Period6/03/098/03/09

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