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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >A Comparison of Texture Models for Automatic Liver Segmentation
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A Comparison of Texture Models for Automatic Liver Segmentation

机译：自动肝分割纹理模型的比较

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摘要

Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture information; this texture information is expected to be homogenous and consistent across multiple slices for the same organ. Our proposed approach consists of the following steps: first, we perform pixel-level texture extraction; second, we generate liver probability images using a binary classification approach; third, we apply a split-and-merge algorithm to detect the seed set with the highest probability area; and fourth, we apply to the seed set a region growing algorithm iteratively to refine the liver's boundary and get the final segmentation results. Furthermore, we compare the segmentation results from three different texture extraction methods (Co-occurrence Matrices, Gabor filters, and Markov Random Fields (MRF)) to find the texture method that generates the best liver segmentation. From our experimental results, we found that the co-occurrence model led to the best segmentation, while the Gabor model led to the worst liver segmentation. Moreover, co-occurrence texture features alone produced approximately the same segmentation results as those produced when all the texture features from the combined co-occurrence, Gabor, and MRF models were used. Therefore, in addition to providing an automatic model for liver segmentation, we also conclude that Haralick cooccurrence texture features are the most significant texture characteristics in distinguishing the liver tissue in CT scans.
机译：由于灰度范围的重叠以及软组织的位置和形状的变化，仅基于灰度或形状从腹部计算机断层扫描（CT）图像进行自动肝分割是困难的。为了解决这些问题，我们提出了一种自动肝分割方法，该方法利用基于纹理信息的低级特征。对于同一器官，该纹理信息在多个切片上是均匀且一致的。我们提出的方法包括以下步骤：首先，我们执行像素级纹理提取。其次，我们使用二元分类方法生成肝脏概率图像。第三，我们采用分裂合并算法来检测具有最高概率区域的种子集。第四，我们将种子区域迭代算法应用于种子集，以细化肝脏边界并获得最终的分割结果。此外，我们比较了三种不同纹理提取方法（共现矩阵，Gabor滤波器和马尔可夫随机场（MRF））的分割结果，以找到能够产生最佳肝脏分割的纹理方法。从我们的实验结果中，我们发现共现模型导致最佳分割，而Gabor模型导致最差的肝脏分割。此外，共生纹理特征单独产生的分割结果与使用组合的共生，Gabor和MRF模型的所有纹理特征产生的分割结果几乎相同。因此，除了提供自动肝分割模型外，我们还得出结论，Haralick共生纹理特征是在CT扫描中区分肝脏组织时最重要的纹理特征。

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