Medical image segmentation with transform and moment based features and incremental supervised neural network

İŞCAN Z., Güvenilir F. Y., Ölmez Z., Korürek M., Ölmez T.

Digital Signal Processing: A Review Journal, vol.19, no.5, pp.890-901, 2009 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 5
  • Publication Date: 2009
  • Doi Number: 10.1016/j.dsp.2009.03.001
  • Journal Name: Digital Signal Processing: A Review Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.890-901
  • Keywords: Continuous wavelet transform, Incremental neural networks, Medical image segmentation, Statistical moments, Supervised learning
  • Istanbul Medipol University Affiliated: No

Abstract

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images. © 2009 Elsevier Inc. All rights reserved.