Quantitative analysis of breast echotexture patterns in automated breast ultrasound images.

Journal: Medical physics
Published Date: Aug 1, 2015

Abstract

PURPOSE: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images.

Authors

  • Ruey-Feng Chang
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
  • Yu-Ling Hou
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan.
  • Chung-Ming Lo
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
  • Chiun-Sheng Huang
    Department of Surgery, National Taiwan University Hospital, Taipei 10617, Taiwan.
  • Jeon-Hor Chen
    Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan and Tu and Yuen Center for Functional Onco-Imaging and Department of Radiological Science, University of California, Irvine, California 92697.
  • Won Hwa Kim
    Dept. of Computer Sciences, University of Wisconsin, Madison, WI, U.S.A.
  • Jung Min Chang
    Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
  • Min Sun Bae
    Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
  • Woo Kyung Moon
    Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.

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