Approaches for automated detection and classification of masses in mammograms

  • Authors:

  • H. D. Cheng;X. J. Shi;R. Min;L. M. Hu;X. P. Cai;H. N. Du

  • Affiliations:

  • Department of Computer Science, 401B, Old Main Hall, Utah State University, Logan, UT 84322-4205, USA;Department of Computer Science, 401B, Old Main Hall, Utah State University, Logan, UT 84322-4205, USA;Department of Computer Science, 401B, Old Main Hall, Utah State University, Logan, UT 84322-4205, USA;Department of Computer Science, 401B, Old Main Hall, Utah State University, Logan, UT 84322-4205, USA;Department of Computer Science, 401B, Old Main Hall, Utah State University, Logan, UT 84322-4205, USA;Department of Computer Science, 401B, Old Main Hall, Utah State University, Logan, UT 84322-4205, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2006

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Abstract

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The estimated sensitivity of radiologists in breast cancer screening is only about 75%, but the performance would be improved if they were prompted with the possible locations of abnormalities. Breast cancer CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This paper discusses the methods for mass detection and classification, and compares their advantages and drawbacks.