Model-based segmentation of medical imagery by matching distributions
Model-based segmentation of medical imagery by matching distributions
复制标题
DOI：
10.1109/tmi.2004.841228
复制
发表时间：
2005-03-01
影响因子：
10.6
通讯作者：
Chen, GTY
中科院分区：
文献类型：
--
作者：
Freedman, D;Radke, RJ;Chen, GTY
从三维（3-D）图像中分割可变形物体是一个重要而具有挑战性的问题，特别是在医学图像的背景下。我们提出了一种新的分割算法，该算法基于匹配概率分布的光度变量，结合了感兴趣对象的学习形状和外观模型。类似方法的主要创新之处在于不需要计算模型和图像之间的像素对应关系。这允许一个快速的、有原则的算法。我们提出了有希望的结果，困难的图像为三维计算机断层扫描图像的男性骨盆为目的的图像引导放射治疗的前列腺。
The segmentation of deformable objects from three-dimensional (3-D) images is an important and challenging problem, especially in the context of medical imagery. We present a new segmentation algorithm based on matching probability distributions of photometric variables that incorporates learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image. This allows for a fast, principled algorithm. We present promising results on difficult imagery for 3-D computed tomography images of the male pelvis for the purpose of image-guided radiotherapy of the prostate.