Contour segmentation in 2D ultrasound medical images with particle filtering.

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 1432-1769

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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.

Item Type:
Journal Article
Journal or Publication Title:
Machine Vision and Applications
Additional Information:
The original publication is available at www.springerlink.com
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1708
Subjects:
?? ultrasound (us) image segmentation · contour trackingbayesian inferencesequential monte carlo methodsparticle filter (pf)speckle noisehardware and architecturesoftwarecomputer vision and pattern recognitioncomputer science applicationsqa75 electronic comp ??
ID Code:
32892
Deposited By:
Deposited On:
26 Apr 2010 08:36
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 10:56