Angelova, D. and Mihaylova, L. (2011) Contour segmentation in 2D ultrasound medical images with particle filtering. Machine Vision and Applications, 22 (3). 551 - 561. ISSN 0932-8092
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2011 Springer. This is an author produced version of a paper subsequently published in Machine Vision and Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Ultrasound (US) image segmentation; Contour tracking; Bayesian inference; Sequential Monte Carlo methods; Particle filter (PF); Speckle noise |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Dec 2014 10:26 |
Last Modified: | 28 Mar 2018 17:36 |
Published Version: | http://dx.doi.org/10.1007/s00138-010-0261-4 |
Status: | Published |
Publisher: | Springer |
Refereed: | No |
Identification Number: | 10.1007/s00138-010-0261-4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82278 |