Automated characterization of breast cancer using steerable filters

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

doi:10.3233/978-1-61499-800-6-321

Automated characterization of breast cancer using steerable filters

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

^*Corresponding author for this work

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

3 Citations (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 language	English
Title of host publication	New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017
Editors	Hamido Fujita, Ali Selamat, Sigeru Omatu
Publisher	IOS Press
Pages	321-327
Number of pages	7
Volume	297
ISBN (Electronic)	9781614997993
DOIs	https://doi.org/10.3233/978-1-61499-800-6-321
Publication status	Published - 2017
Event	16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017 - Kitakyushu, Japan Duration: 26-09-2017 → 28-09-2017

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	297
ISSN (Print)	0922-6389

Conference

Conference	16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017
Country/Territory	Japan
City	Kitakyushu
Period	26-09-17 → 28-09-17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Access to Document

10.3233/978-1-61499-800-6-321

Cite this

Fujita, H., Raghavendra, U., Gudigar, A., Vadakkepat, V. V., & Acharya, U. R. (2017). Automated characterization of breast cancer using steerable filters. In H. Fujita, A. Selamat, & S. Omatu (Eds.), 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

Fujita, Hamido ; Raghavendra, U. ; Gudigar, Anjan et al. / Automated characterization of breast cancer using steerable filters. New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017. editor / Hamido Fujita ; Ali Selamat ; Sigeru Omatu. Vol. 297 IOS Press, 2017. pp. 321-327 (Frontiers in Artificial Intelligence and Applications).

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title = "Automated characterization of breast cancer using steerable filters",

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.",

author = "Hamido Fujita and U. Raghavendra and Anjan Gudigar and Vadakkepat, \{Vinoy Vishnu\} and Acharya, \{U. Rajendra\}",

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year = "2017",

doi = "10.3233/978-1-61499-800-6-321",

language = "English",

volume = "297",

series = "Frontiers in Artificial Intelligence and Applications",

publisher = "IOS Press",

pages = "321--327",

editor = "Hamido Fujita and Ali Selamat and Sigeru Omatu",

booktitle = "New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017",

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}

Fujita, H, Raghavendra, U , Gudigar, A, Vadakkepat, VV & Acharya, UR 2017, Automated characterization of breast cancer using steerable filters. in H Fujita, A Selamat & S Omatu (eds), New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017. vol. 297, Frontiers in Artificial Intelligence and Applications, vol. 297, IOS Press, pp. 321-327, 16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017, Kitakyushu, Japan, 26-09-17. https://doi.org/10.3233/978-1-61499-800-6-321

Automated characterization of breast cancer using steerable filters. / Fujita, Hamido; Raghavendra, U.; Gudigar, Anjan et al.
New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017. ed. / Hamido Fujita; Ali Selamat; Sigeru Omatu. Vol. 297 IOS Press, 2017. p. 321-327 (Frontiers in Artificial Intelligence and Applications; Vol. 297).

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

TY - GEN

T1 - Automated characterization of breast cancer using steerable filters

AU - Fujita, Hamido

AU - Raghavendra, U.

AU - Gudigar, Anjan

AU - Vadakkepat, Vinoy Vishnu

AU - Acharya, U. Rajendra

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

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U2 - 10.3233/978-1-61499-800-6-321

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M3 - Conference contribution

AN - SCOPUS:85029217412

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BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017

A2 - Fujita, Hamido

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Y2 - 26 September 2017 through 28 September 2017

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

Automated characterization of breast cancer using steerable filters

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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