Interpretation of MR images using self-organizing maps and knowledge-based expert systems

Gueler I., Demirhan A., Karakış R.

DIGITAL SIGNAL PROCESSING, cilt.19, sa.4, ss.668-677, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 4
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.dsp.2008.08.002
  • Dergi Adı: DIGITAL SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.668-677
  • Sivas Cumhuriyet Üniversitesi Adresli: Hayır

Özet

A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities. first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions. (c) 2008 Elsevier Inc. All rights reserved.