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


Iscan Z., YUKSEL A., DOKUR Z., KORUREK M., Ölmez T.

DIGITAL SIGNAL PROCESSING, cilt.19, sa.5, ss.890-901, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 5
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.dsp.2009.03.001
  • Dergi Adı: DIGITAL SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.890-901
  • Anahtar Kelimeler: Medical image segmentation, Incremental neural networks, Supervised learning, Statistical moments, Continuous wavelet transform, FEATURE-EXTRACTION, WAVELET TRANSFORM, CLASSIFICATION, SOUNDS, SYSTEM
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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. (C) 2009 Elsevier Inc. All rights reserved.