Automated characterization of breast cancer using steerable filters

Hamido Fujita, U. Raghavendra, Anjan Gudigar, Vinoy Vishnu Vadakkepat, U. Rajendra Acharya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Breast cancer is one of the highly researched topic in medical image analysis. Digital mammogram analysis is one of the techniques which helps in determining severity of breast cancer within the context of medical image analysis. In this work, a novel technique using steerable co-occurrence features and the independent component analysis (ICA) is proposed. Our method is evaluated using 1000 mammogram images and can efficiently classify normal, benign and malignant classes with a promising performance of 88.60% accuracy, using only ten features. The proposed method is completely automatic and it does not require any segmentation technique in the breast region.

Original languageEnglish
Title of host publicationNew Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017
PublisherIOS Press
Pages321-327
Number of pages7
Volume297
ISBN (Electronic)9781614997993
DOIs
Publication statusPublished - 2017
Event16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017 - Kitakyushu, Japan
Duration: 26-09-201728-09-2017

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume297
ISSN (Print)0922-6389

Conference

Conference16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017
CountryJapan
CityKitakyushu
Period26-09-1728-09-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Fujita, H., Raghavendra, U., Gudigar, A., Vadakkepat, V. V., & Acharya, U. R. (2017). Automated characterization of breast cancer using steerable filters. In New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017 (Vol. 297, pp. 321-327). (Frontiers in Artificial Intelligence and Applications; Vol. 297). IOS Press. https://doi.org/10.3233/978-1-61499-800-6-321