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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 >Automatic Segmentation of Colonic Polyps in CT Colonography Based on Knowledge-Guided Deformable Models
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Automatic Segmentation of Colonic Polyps in CT Colonography Based on Knowledge-Guided Deformable Models

机译：基于知识指导的可变形模型的CT结肠造影结肠息肉自动分割

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摘要

An automatic method to segment colonic polyps from CT colonography is presented. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy c-mean clustering, and deformable models. The input is a set of polyp seed points generated by filters on geometric properties of the colon surface. First, the potential polyp region is enhanced by a knowledge-guided adjustment. Then, a fuzzy c-mean clustering is applied on a 64*64 pixel sub-image around the seed. Fuzzy membership functions for lumen air, polyp tissues and other tissues are computed for each pixel. Finally, the gradient of the fuzzy membership function is used as the image force to drive a deformable model to the polyp boundary. The segmentation process is first executed on the 2D transverse slice where the polyp seed is located, and then is propagated to neighboring slices to construct a 3D representation of the polyp. Manual segmentation is performed on the same polyps and treated as the ground truth. The automatically generated segmentation is compared with the ground truth segmentation to validate the accuracy of the method. Experimental results showed that the average overlap between the automatic segmentation and manual segmentation is 76.3%. Given the complex polyp boundaries and the small size of the polyp, this is a good result both visually and quantitatively.
机译：提出了一种从CT结肠造影术中分割结肠息肉的自动方法。该方法基于知识导向的强度调整，模糊c均值聚类和可变形模型的组合。输入是一组由结肠表面几何特性的过滤器生成的息肉种子点。首先，潜在的息肉区域通过知识指导的调整得到增强。然后，将模糊c均值聚类应用于种子周围的64 * 64像素子图像。为每个像素计算管腔空气，息肉组织和其他组织的模糊隶属度函数。最后，将模糊隶属度函数的梯度用作图像力，以将可变形模型驱动到息肉边界。分割过程首先在息肉种子所在的2D横向切片上执行，然后传播到相邻切片以构造息肉的3D表示。在相同的息肉上进行手动分割，并视为基本事实。将自动生成的分割与地面真实分割进行比较，以验证该方法的准确性。实验结果表明，自动分割和手动分割之间的平均重叠率为76.3％。考虑到复杂的息肉边界和息肉的小尺寸，这在视觉和定量上都是一个很好的结果。

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