Discriminant Analysis Based Level Set Segmentation for Ultrasound Imaging
Segmentation is one of the fundamental tasks in computer vision applications. The nature of ultrasound images, which are subject to multiplicative noise instead of the widely used additive noise modeling, leads to problems of standard segmentation algorithms. In this paper we propose a new level set approach for the segmentation of medical ultrasound data. The advantage of this approach is both its simpleness and robustness: the noise inherent in ultrasound images does not have to be modeled explicitly but is rather estimated by means of discriminant analysis. In particular, we determine an optimal threshold, which enables us to separate two signal distributions in the intensity histogram and incorporate this information in the evolution of the level set contour. The superiority of our approach over the popular Chan-Vese formulation is demonstrated on real 2D patient data from echocardiography.
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