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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Liver segmentation combining Gabor filtering and traditional vector field snake
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Liver segmentation combining Gabor filtering and traditional vector field snake

机译：结合Gabor滤波和传统矢量场蛇的肝分割

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This paper presents a study of a more accurately propagating deformable contour for outlining the liver in a Computed Tomography image of the abdomen, relying on the idea that a deformable parametric snake will propagate more accurately to the correct edges of an image when applied to textural information of the image as opposed to simple gray level information. The texture information is quantified using a set of Gabor filters and various methods of curve deformation are investigated, including a traditional vector field, gradient vector flow, and an expanding level-set method. Given the relative similarity in gray values of adjacent soft tissues, we found that a deformation algorithm that provides too large a capture range would be easily distracted by nearby values and therefore unsuitable for the particular task of segmenting the liver. Our results demonstrate both a general increase in performance of snake segmentation across the dataset as well as a significant regional improvement in accuracy, particularly in images corresponding with the top of the liver.
机译：本文提出了一种更精确地传播可变形轮廓的研究，以在腹部计算机断层扫描图像中勾勒出肝脏轮廓，其依据是当将变形参数蛇应用于纹理信息时，它将更准确地传播到图像的正确边缘。与简单的灰度级信息相反的图像。使用一组Gabor滤波器对纹理信息进行量化，并研究了各种曲线变形方法，包括传统的矢量场，梯度矢量流和扩展的水平集方法。给定相邻软组织的灰度值相对相似，我们发现提供捕获范围太大的变形算法很容易被附近的值分散注意力，因此不适合分割肝脏的特定任务。我们的结果表明，在整个数据集中进行蛇分割的性能普遍提高，并且在准确性方面存在明显的区域性提高，尤其是在与肝脏顶部相对应的图像中。

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