Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

DEMİRHAN A., Gueler İ.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.24, sa.2, ss.358-367, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 2
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.engappai.2010.09.008
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.358-367
  • Anahtar Kelimeler: Brain MR images, Image segmentation, Stationary wavelet transform, Self-organizing maps, Learning vector quantization, Tanimoto similarity index, TEXTURE CLASSIFICATION, PACKET FRAME
  • Gazi Üniversitesi Adresli: Evet

Özet

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter. (C) 2010 Elsevier Ltd. All rights reserved.