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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Veröffentlicht in:Pattern recognition letters Jg. 24; H. 4; S. 779 - 790
Hauptverfasser: Boukerroui, Djamal, Baskurt, Atilla, Noble, J.Alison, Basset, Olivier
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2003
Elsevier
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
ISSN:0167-8655
1872-7344
DOI:10.1016/S0167-8655(02)00181-2