Angelova, Donka and Mihaylova, Lyudmila (2011) Contour segmentation in 2D ultrasound medical images with particle filtering. Machine Vision and Applications, 22 (3). pp. 551-561. ISSN 1432-1769Full text not available from this repository.
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.
|Journal or Publication Title:||Machine Vision and Applications|
|Additional Information:||The original publication is available at www.springerlink.com|
|Uncontrolled Keywords:||Ultrasound (US) image segmentation · Contour Tracking ; Bayesian inference ; Sequential Monte Carlo methods ; Particle filter (PF) ; Speckle noise|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited By:||Dr L Mihaylov|
|Deposited On:||26 Apr 2010 09:36|
|Last Modified:||20 Jan 2017 01:40|
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