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A Variation Level Set Formulation Local Based C-V Model for Medical Images Segmentation

机译:基于级别集的局部C-V模型的医学图像分割

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Local image information is crucial for accurate segmentation of images with intensity inhomogeneity which usually occurs in medical images. However, image information in local region is not incorporated in popular region-based active contour models, such as piecewise constant models and piecewise smooth models. In this paper, a method which is able to use local information is proposed. The main point is the introduction of the local fitting information expressed by a kernel function which is crucial for segmentation. Our method is based on piecewise constant Chan-Vese model, and compare with different methods for several synthetic images and medical images.
机译:本地图像信息对于具有强度不均匀性的图像的准确分割至关重要,这通常发生在医学图像中。然而,本地区域中的图像信息未结合在基于流行的基于区域的主动轮廓模型中,例如分段恒定模型和分段平滑模型。在本文中,提出了一种能够使用本地信息的方法。主要观点是引入由内核功能表示的局部拟合信息,这对于分割至关重要。我们的方法基于分段常数Chan-Vese模型,并与多种合成图像和医学图像的不同方法进行比较。

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