A new feature extraction method based on multi-resolution representations of mammograms

Gedik N.

APPLIED SOFT COMPUTING, cilt.44, ss.128-133, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.asoc.2016.04.004
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.128-133
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

In this paper, I introduce a new method for feature extraction to classify digital mammograms using fast finite shearlet transform. Initially, fast finite shearlet transform was performed over mammogram images, and feature vectors were built using coefficients of the transform. In subsequent calculations, features were ranked according to t-test statistics, and capabilities were distinguished between different classes. To maximize differences between class representatives, a thresholding process was implemented as a final stage of feature extraction, and classifications were calculated over the optimal feature set using 5-fold cross validation and a support vector machine (SVM) classifier. The present results show that the proposed method provides satisfactory classification accuracy. (C) 2016 Elsevier B.V. All rights reserved.