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首页> 外文期刊>Journal of visual communication & image representation >Automatic liver segmentation for volume measurement in CT Images
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Automatic liver segmentation for volume measurement in CT Images

机译：自动肝分割以在CT图像中进行体积测量

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Computed tomography (CT) images have been widely used for diagnosis of liver disease and volume measurement for liver surgery or transplantation. Automatic liver segmentation and volume measurement based on the segmentation are the most essential parts in computer-aided diagnosis for liver CT as well as computer-aided surgery. However, liver segmentation, in general, has been performed by outlining the medical image manually or segmenting CT images semi-automat-ically because surface features of the liver and partial-volume effects make automatic discrimination from other adjacent organs or tissues very difficult. Accordingly, in this paper, we propose a new approach to automatic segmentation of the liver for volume measurement in sequential CT images. Our method analyzes the intensity distribution of several abdominal CT samples and exploits a priori knowledge, such as CT numbers and location of the liver to identify coherent regions that correspond to the liver. The proposed scheme utilizes recursively morphological filter with region-labeling and clustering to detect the search range and to generate the initial liver contour. In this search range, we deform liver contour using the labeling-based search algorithm following pattern features of the liver contour. Lastly, volume measurement is automatically performed on the segmented liver regions. The experimental measurement of area and volume is compared with those using manual tracing method as a gold standard by the radiological doctors, and demonstrates that this algorithm is effective for automatic segmentation and volume measurement method of the liver.
机译：计算机断层扫描（CT）图像已被广泛用于肝病的诊断和肝脏手术或移植的体积测量。自动肝分割和基于分割的体积测量是肝脏CT以及计算机辅助手术的计算机辅助诊断中最重要的部分。但是，一般来说，肝脏分割是通过手动勾勒医学图像或半自动分割CT图像来进行的，因为肝脏的表面特征和部分体积效应使自动区分其他相邻器官或组织非常困难。因此，在本文中，我们提出了一种自动分割肝脏的新方法，用于在连续CT图像中进行体积测量。我们的方法分析了几个腹部CT样本的强度分布，并利用先验知识（例如CT数和肝脏的位置）来识别与肝脏相对应的连贯区域。提出的方案利用具有区域标记和聚类的递归形态过滤器来检测搜索范围并生成初始肝脏轮廓。在此搜索范围内，我们遵循肝脏轮廓的模式特征，使用基于标签的搜索算法使肝脏轮廓变形。最后，对分割的肝脏区域自动执行体积测量。放射医生将面积和体积的实验测量值与以手动跟踪方法作为黄金标准的测量结果进行了比较，证明了该算法对于肝脏的自动分割和体积测量方法是有效的。

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