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- W2399136053 abstract "Ultrasound (US) image segmentation is one of the most difficult and challenging among medical imaging modalities due to the poor signal-to-noise ratio, signal dropouts, and speckle patterns characteristic of US images. Previous methods avoided the use of purely intensity-based segmentation approaches, because of the intensity inhomogeneities present within the structures of interest. However, local phase, derived from the monogenic signal, extracts structural information from US images, being invariant to contrast. By combining different scales of the local phase, feature asymmetry can be calculated to represent edge information. This paper proposes a novel ultrasound segmentation approach based on the fuzzy connectedness framework. A new affinity function is designed to drive the segmentation algorithm using structural and edge information based on local phase, instead of intensities and intensity gradients. Quantitative and qualitative results are illustrated on US images of the fetal arm, the object of interest being the adipose tissue, which is an indicator of fetal nutrition." @default.
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- W2399136053 date "2011-01-01" @default.
- W2399136053 modified "2023-09-26" @default.
- W2399136053 title "Local Phase-Based Fuzzy Connectedness Segmentation of Ultrasound Images." @default.
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