Low Level Feature Selection for a Content Based Digital Mammography Image Retrieval System

ÖZTÜRK Ö., Bulu H., ALPKOÇAK A., Guzelis C.

IEEE 17th Signal Processing and Communications Applications Conference, Antalya, Türkiye, 9 - 11 Nisan 2009, ss.682-683 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2009.5136428
  • Basıldığı Şehir: Antalya
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.682-683
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Content Based Image Retrieval (CBIR) systems enables to retrieve images from large image archieves based on its contents as well as external attributes associated to each image. This study aims at extracting low level attributes to be used in a CBIR model that enables the utilization of low, level image based attributes together with high level concepts. The contribution of this study is to develop an infrastructure for the selection of best low level attribute set to be used in the CBIR method by considering model performance. Within the scope of this study: segmentation of mammogram images, development of a mammogram database, low level attribute extraction from the segmented images and breast type estimation by means of machine learning algorithms are realized.