An incremental neural network for tissue segmentation in ultrasound images

Mehmet Nadir Kurnaz*, Zümray Dokur, Tamer Ölmez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4 × 4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4 × 4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.

Original languageEnglish
Pages (from-to)187-195
Number of pages9
JournalComputer Methods and Programs in Biomedicine
Volume85
Issue number3
DOIs
Publication statusPublished - Mar 2007

Keywords

  • Feature extraction
  • Image segmentation
  • Incremental neural network
  • Texture analysis
  • Ultrasound

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