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Machine Vision and Applications

, Volume 22, Issue 3, pp 551–561 | Cite as

Contour segmentation in 2D ultrasound medical images with particle filtering

  • Donka Angelova
  • Lyudmila Mihaylova
Original Paper

Abstract

Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images. Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise.

Keywords

Ultrasound (US) image segmentation Contour tracking Bayesian inference Sequential Monte Carlo methods Particle filter (PF) Speckle noise 

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Institute for Parallel ProcessingBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of Communication SystemsLancaster UniversityLancasterUK

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