Classification of Salient Dense Regions in Mammograms based on the Minimum Nesting Depth Approach

Ture H., KAYIKÇIOĞLU T.

23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 16 - 19 May 2015, pp.2174-2177 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2015.7130304
  • City: Malatya
  • Country: Turkey
  • Page Numbers: pp.2174-2177
  • Karadeniz Technical University Affiliated: Yes

Abstract

In this study, a novel method for classifying salient dense regions in mammograms is proposed. The method respectively includes detecting threshold based local maximum regions, eliminated with the decision tree process, computing features and minimum nesting depths for candidates of region of interests and finally classification by using Support Vector Machines (SVM). Experimental results demonstrate that the proposed method achieve good performance for detecting masses in mammogram.