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首页> 外文会议>Conference on Physiology and Function: Methods, Systems, and Applications Feb 16-18, 2003 San Diego, California, USA >A probabilistic method for virtual colonoscopy cleansing
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A probabilistic method for virtual colonoscopy cleansing

机译：虚拟结肠镜清洗的概率方法

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摘要

Currently, virtual colonoscopy examinations require extensive bowel preparation because residual materials can occlude lesions or can be misinterpreted as polyps. Our goal is to investigate a probabilistic method to segment contrast enhanced residual materials and remove them from the rendering. The region around a sample position is modeled to contain mixtures of air, tissue and tagged intraluminal remains. For each image sample a probability vector is calculated expressing the probability that the materials of interest are present. A probability space is defined using the probabilities for pure materials as base vectors. Mixture vectors are constructed at 45-degree angles between the pure material vectors. The probability vectors are compared to the base vectors and the mixture vectors to classify them into material mixtures. Consider the layer between air and tagged fluid. Image intensities are similar to tissue. The scale at which the Gaussian averaged probability is calculated is increased until convergence: two successive scales result in the same classification. The Bayesian classification method shows good results with relatively large objects. However, edges of small or thin objects are likely to be misclassified: a too large environment is needed for convergence.
机译：当前，虚拟结肠镜检查需要大量的肠道准备，因为残留的物质会阻塞病变或被误认为息肉。我们的目标是研究一种概率方法来分割对比度增强的残差材料并将其从渲染中移除。对样品位置周围的区域进行建模以包含空气，组织和带标签的腔内残留物的混合物。对于每个图像样本，计算一个概率向量，表示存在感兴趣材料的概率。使用纯材料的概率作为基本向量来定义概率空间。在纯物质矢量之间以45度角构造混合矢量。将概率向量与基本向量和混合向量进行比较，以将其分类为材料混合。考虑空气和标记流体之间的层。图像强度类似于组织。计算高斯平均概率的标度会增加，直到收敛为止：两个连续的标度导致相同的分类。贝叶斯分类方法对于较大的对象显示出良好的结果。但是，小物体或薄物体的边缘可能会被错误分类：会聚需要太大的环境。

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