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Liver segmentation using automatically defined patient specific B-Spline surface models

Song, Y., Bulpitt, A.J. and Brodlie, K.W. (2009) Liver segmentation using automatically defined patient specific B-Spline surface models. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009 : 12th International Conference, London, UK, September 20-24, 2009, Proceedings. Lecture Notes in Computer Science (5762). Springer , Tiergartenstraße 17, 69121 Heidelberg, Germany , pp. 43-50. ISBN 978-3-642-04270-6

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Abstract

This paper presents a novel liver segmentation algorithm. This is a model-driven approach; however, unlike previous techniques which use a statistical model obtained from a training set, we initialize patient-specific models directly from their own pre-segmentation. As a result, the non-trivial problems such as landmark correspondences, model registration etc. can be avoided. Moreover, by dividing the liver region into three sub-regions, we convert the problem of building one complex shape model into constructing three much simpler models, which can be fitted independently, greatly improving the computation efficiency. A robust graph-based narrow band optimal surface fitting scheme is also presented. The proposed approach is evaluated on 35 CT images. Compared to contemporary approaches, our approach has no training requirement and requires significantly less processing time, with an RMS error of 2.440.53mm against manual segmentation.

Item Type: Book Section
Copyright, Publisher and Additional Information: © 2009 Springer-Verlag Berlin Heidelberg. This is an author produced version of a paper published in 'Lecture Notes in Computer Science'. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: liver segmentation, patient-specific model, level set, deformable model, B-Spline,
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering (Leeds) > School of Computing (Leeds)
Depositing User: Dr Yi Song
Date Deposited: 11 Dec 2009 11:50
Last Modified: 08 Feb 2013 17:06
Published Version: http://dx.doi.org/10.1007/978-3-642-04271-3_6
Status: Published
Publisher: Springer
Refereed: Yes
Identification Number: 10.1007/978-3-642-04271-3_6
URI: http://eprints.whiterose.ac.uk/id/eprint/10248

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