Segmentation of ultrasound images––multiresolution 2D and 3D algorithm based on global and local statistics

In this paper, we propose a robust adaptive region segmentation algorithm for noisy images, within a Bayesian framework. A multiresolution implementation of the algorithm is performed using a wavelets basis and can be used to process both 2D and 3D data. In this work we focus on the adaptive charact...

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Published in:	Pattern recognition letters Vol. 24; no. 4; pp. 779 - 790
Main Authors:	Boukerroui, Djamal, Baskurt, Atilla, Noble, J.Alison, Basset, Olivier
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2003 Elsevier
Subjects:	Adaptive algorithm Bayesian segmentation Computer Science Medical Imaging Multiresolution Ultrasound Bayesian segmentation Adaptive algorithm Multiresolution Ultrasound imagerie_volumique Imagerie Ultrasonore Images et Modèles categₛt2i imagerie_ultrasonore
ISSN:	0167-8655, 1872-7344
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Abstract	In this paper, we propose a robust adaptive region segmentation algorithm for noisy images, within a Bayesian framework. A multiresolution implementation of the algorithm is performed using a wavelets basis and can be used to process both 2D and 3D data. In this work we focus on the adaptive character of the algorithm and we discuss how global and local statistics can be utilised in the segmentation process. We propose an improvement on the adaptivity by introducing an enhancement to control the adaptive properties of the segmentation process. This takes the form of a weighting function accounting for both local and global statistics, and is introduced in the minimisation. A new formulation of the segmentation problem allows us to control the effective contribution of each statistical component. The segmentation algorithm is demonstrated on synthetic data, 2D breast ultrasound data and on echocardiographic sequences (2D+T). An evaluation of the performance of the proposed algorithm is also presented.
AbstractList	In this paper, we propose a robust adaptive region segmentation algorithm for noisy images, within a Bayesian framework. A multiresolution implementation of the algorithm is performed using a wavelets basis and can be used to process both 2D and 3D data. In this work we focus on the adaptive character of the algorithm and we discuss how global and local statistics can be utilised in the segmentation process. We propose an improvement on the adaptivity by introducing an enhancement to control the adaptive properties of the segmentation process. This takes the form of a weighting function accounting for both local and global statistics, and is introduced in the minimisation. A new formulation of the segmentation problem allows us to control the effective contribution of each statistical component. The segmentation algorithm is demonstrated on synthetic data, 2D breast ultrasound data and on echocardiographic sequences (2D+T). An evaluation of the performance of the proposed algorithm is also presented.
Author	Baskurt, Atilla Noble, J.Alison Boukerroui, Djamal Basset, Olivier
Author_xml	– sequence: 1 givenname: Djamal surname: Boukerroui fullname: Boukerroui, Djamal email: djamal@robots.ox.ac.uk organization: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK – sequence: 2 givenname: Atilla surname: Baskurt fullname: Baskurt, Atilla email: abaskurt@ligim.univ-lyonl.fr organization: LIGIM (EA 1899), Claude Bernard University Lyon 1, Villeurbanne Cedex 69622, France – sequence: 3 givenname: J.Alison surname: Noble fullname: Noble, J.Alison email: noble@robots.ox.ac.uk organization: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK – sequence: 4 givenname: Olivier surname: Basset fullname: Basset, Olivier email: olivier.basset@creatis.univ-lyon1.fr organization: CREATIS CNRS Research Unit (UMR 5515) and affiliated to INSERM, INSA-502 Villeurbanne Cedex 69621, France
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Title	Segmentation of ultrasound images––multiresolution 2D and 3D algorithm based on global and local statistics
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