research-article

A new Fourier-based approach to measure irregularity of breast masses in mammograms

Authors:

Gensheng Zhang,

Hyung D. ChoiAuthors Info & Claims

RACS '12: Proceedings of the 2012 ACM Research in Applied Computation Symposium

Pages 153 - 157

https://doi.org/10.1145/2401603.2401638

Published: 23 October 2012 Publication History

Abstract

Morphologic appearance is one of intuitive diagnosis factors of mass lesions in breast imaging, and irregular shape is one of the most frequent appearances for malignant masses. Thus, an effective measure of morphological irregularity will provide a helpful reference to determine malignancy of breast masses. In this paper, a new measure based on Fourier Transform, named Fourier Irregularity Index (F_II), was developed to provide a reliable malignant/benign classification factor. The experiment was conducted with 418 breast masses, including 190 malignant cases and 218 benign cases. Performance was assessed and compared among various methods using Receiver Operating Characteristics (ROC) analysis. The proposed measure in this study achieved malignant/benign classification accuracy of 96% with an area (A_z) of 0.99 under the receiver operating characteristics (ROC) curve, which outperformed typical traditional approaches, such as Compactness (accuracy of 90%, A_z = 0.96), Fractal Dimension (accuracy of 90%, A_z = 0.95), Fourier Factor (accuracy of 90%, A_z = 0.97), and Fractional Concavity (accuracy of 75%, A_z = 0.65).

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Digital Library

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Cited By

Zhang GWang WShin SHruska CSon S(2015)Fourier irregularity indexMultimedia Tools and Applications10.1007/s11042-013-1799-874:11(3783-3798)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s11042-013-1799-8
Kim HShin SWang WJeon SSuen CAghdam AGuo MHong JNadimi E(2013)SVM-based Harris corner detection for breast mammogram image normal/abnormal classificationProceedings of the 2013 Research in Adaptive and Convergent Systems10.1145/2513228.2513324(187-191)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1145/2513228.2513324

Index Terms

A new Fourier-based approach to measure irregularity of breast masses in mammograms
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
        Shape representations

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Published In

cover image ACM Other conferences

RACS '12: Proceedings of the 2012 ACM Research in Applied Computation Symposium

October 2012

488 pages

ISBN:9781450314923

DOI:10.1145/2401603

General Chairs:
Yookun Cho
Seoul National University, Korea
,
Rex E. Gantenbein
University of Wyoming, USA
,
Tei-Wei Kuo
National Taiwan University, Taiwan
,
Program Chair:
Vahid Tarokh
Harvard University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 October 2012

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RACS '12: Research in Applied Computation Symposium

October 23 - 26, 2012

Texas, San Antonio

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Cited By

Zhang GWang WShin SHruska CSon S(2015)Fourier irregularity indexMultimedia Tools and Applications10.1007/s11042-013-1799-874:11(3783-3798)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s11042-013-1799-8
Kim HShin SWang WJeon SSuen CAghdam AGuo MHong JNadimi E(2013)SVM-based Harris corner detection for breast mammogram image normal/abnormal classificationProceedings of the 2013 Research in Adaptive and Convergent Systems10.1145/2513228.2513324(187-191)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1145/2513228.2513324

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