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...
| Main Authors: | , , , |
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| Format: | Journal article |
| Published: |
2003
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| Summary: | 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. © 2002 Elsevier Science B.V. All rights reserved. |
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