کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
504520 | 864312 | 2011 | 15 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging](/preview/png/504520.png)
In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images.Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic–semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results.
Journal: Computerized Medical Imaging and Graphics - Volume 35, Issue 1, January 2011, Pages 16–30