A comparison of texture models for automatic liver segmentation
A comparison of texture models for automatic liver segmentation
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DOI：
10.1117/12.710422
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发表时间：
2007-03
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影响因子：
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通讯作者：
M. Pham;Ruchaneewan Susomboon;Tim Disney;D. Raicu;Jacob Furst
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文献类型：
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作者：
M. Pham;Ruchaneewan Susomboon;Tim Disney;D. Raicu;Jacob Furst
由于软组织的灰度范围重叠以及位置和形状的变化，从腹部CT图像中自动分割肝脏是困难的。为了解决这些问题，我们提出了一种自动肝脏分割方法，利用低层次的特征，基于纹理信息，这种纹理信息预计是均匀的，并在多个切片相同的器官一致。我们所提出的方法包括以下步骤：第一，我们执行像素级纹理提取;第二，我们使用二进制分类方法生成肝脏概率图像;第三，我们应用分裂和合并算法来检测具有最高概率区域的种子集;第四，我们应用于种子集的区域生长算法迭代地细化肝脏的边界，并得到最终的分割结果。此外，我们比较了三种不同的纹理提取方法（共生矩阵，Gabor滤波器和马尔可夫随机场（MRF））的分割结果，以找到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，共生模型导致最好的分割，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大致相同的分割结果时，产生的所有纹理特征的组合共生，Gabor和MRF模型被使用。因此，除了提供一个自动模型，肝脏分割，我们还得出结论，Haralick共生纹理特征是最重要的纹理特征，在区分肝脏组织的CT扫描。
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.