Segmentation of medical images by using wavelet transform and incremental self-organizing map

Ölmez Z., İŞCAN Z., Ölmez T.

5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence, Apizaco, Mexico, 13 - 17 November 2006, vol.4293 LNAI, pp.800-809 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 4293 LNAI
  • Doi Number: 10.1007/11925231_76
  • City: Apizaco
  • Country: Mexico
  • Page Numbers: pp.800-809
  • Keywords: Artificial neural networks, Segmentation of medical images, Self-organizing map, Wavelet transform
  • Istanbul Medipol University Affiliated: No


This paper presents a novel method that uses incremental self-organizing map (ISOM) network and wavelet transform together for the segmentation of magnetic resonance (MR), computer tomography (CT) and ultrasound (US) images. In order to show the validity of the proposed scheme, ISOM has been compared with Kohonen's SOM. Two-dimensional continuous wavelet transform (2D-CWT) is used to form the feature vectors of medical images. According to the selected two feature extraction methods, features are formed by the intensity of the pixel of interest or mean value of intensities at one neighborhood of the pixel at each sub-band. The first feature extraction method is used for MR and CT head images. The second method is used for US prostate image. © Springer-Verlag Berlin Heidelberg 2006.