An early vision-based snake model for ultrasound image segmentation

Chung Ming Chen*, Henry Horng Shing Lu, Yu Chen Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the early-vision model and the discrete-snake model. By simulating human early vision, the early-vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the initial contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model. (C) 2000 World Federation for Ultrasound in Medicine and Biology.

Original languageEnglish
Pages (from-to)273-285
Number of pages13
JournalUltrasound in Medicine and Biology
Volume26
Issue number2
DOIs
StatePublished - 1 Feb 2000

Keywords

  • Discrete-snake model
  • Early-vision model
  • Image segmentation
  • Snake
  • Speckles
  • Texture
  • Ultrasound

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