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首页> 外文会议>National Conference on Biomedical Engineering;International Iranian Conference on Biomedical Engineering >Automatic Liver Segmentation in MR and CE-MR Images with LCVAC - GAC Approach Using Mean- shape Initialization Technique
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Automatic Liver Segmentation in MR and CE-MR Images with LCVAC - GAC Approach Using Mean- shape Initialization Technique

机译：使用均值形状初始化技术的LCVAC-GAC方法在MR和CE-MR图像中自动进行肝分割

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The Volume of the liver is a determining factor in measuring the severity of liver diseases and should be monitored regularly. Consequently, liver segmentation and volume estimation using image processing techniques play an important role in the follow up procedure. Automated liver segmentation is a challenging problem mostly addressed in CT images. MR imaging is preferred by radiologists for follow-up procedures since it does not expose patients to ionizing radiation and provides higher resolution. However, fewer studies report liver segmentation in MR images. MRI Liver segmentation represents a challenge due to presence of characteristic artifacts, such as partial volumes, noise, low contrast and poorly defined edges of the liver with respect to adjacent organs. In the present study we introduced a new automatic algorithm to 3D liver segmentation for MR and CE-MR images. The proposed algorithm includes a liver mean- shape that provides an automatic initialization together with a novel active contour method based on region with edge-weight and edge terms. In addition, different areas of liver were found, and dependent parameters were calculated automatically using modern geodesic function. We tested our algorithm on images acquired from 54 subjects in two hospitals. Finally, the results of the proposed method were compared with those of two conventional active contour methods.
机译：肝脏体积是衡量肝脏疾病严重程度的决定因素，应定期进行监测。因此，使用图像处理技术进行肝脏分割和体积估计在随访过程中起着重要作用。自动肝分割是一个具有挑战性的问题，主要在CT图像中解决。放射线学家不愿意将MR成像用于后续程序，因为它不会使患者暴露于电离辐射下并且可以提供更高的分辨率。但是，很少有研究报道MR图像中的肝脏分割。 MRI肝脏分割是一项挑战，因为存在特征性伪影，例如部分体积，噪声，低对比度以及相对于相邻器官的肝脏边缘不清晰。在本研究中，我们为MR和CE-MR图像引入了一种新的自动算法进行3D肝脏分割。所提出的算法包括肝脏均值形状，该均值形状提供自动初始化以及基于具有边缘权重和边缘项的区域的新颖的主动轮廓方法。此外，发现了肝脏的不同区域，并使用现代测地函数自动计算了相关参数。我们在两家医院的54位受试者身上采集的图像上测试了我们的算法。最后，将所提出的方法的结果与两种传统的主动轮廓方法的结果进行了比较。

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