Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures | Academic Article individual record
abstract

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.

publication outlet

The São Paulo Journal of Mathematical Sciences

author list (cited authors)
Jeon, S., Nicolis, O., & Vidakovic, B.
publication date
2014
keywords
  • Breast Cancer
  • Cancer
  • Biomedical Imaging
  • Prevention
citation count

9