Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

  • Seonghye Jeon Depertment of Industrial and Systems Engineeroing, Georgia Institute of Technology, US
  • Orietta Nicolis Instituto de Estadistica, Universidad de Valparaiso, Chile
  • Brani Vidakovic Depertment of Industrial and Systems Engineeroing, Georgia Institute of Technology, US

Resumo

Breast cancer is the second leading cause of death in women in the United States. Mammography is currently the most eective method for detecting breast cancer early; however, radiological inter- pretation of mammogram images is a challenging task. Many medical images demonstrate a certain degree of self-similarity over a range of scales. This scaling can help us to describe and classify mammograms. In this work, we generalize the scale-mixing wavelet spectra to the complex wavelet domain. In this domain, we estimate Hurst parameter and image phase and use them as discriminatory descriptors to clas- sify mammographic images to benign and malignant. The proposed methodology is tested on a set of images from the University of South Florida Digital Database for Screening Mammography (DDSM). Keywords: Scaling; Complex Wavelets; Self-similarity; 2-D Wavelet Scale-Mixing Spectra.

Downloads

Não há dados estatísticos.
Publicado
2014-12-12
Como Citar
Jeon, S., Nicolis, O., & Vidakovic, B. (2014). Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures. São Paulo Journal of Mathematical Sciences, 8(2), 265-284. https://doi.org/10.11606/issn.2316-9028.v8i2p265-284
Seção
Artigos