Adaptive local window for level set segmentation of CT and MRI liver lesions.
Adaptive local window for level set segmentation of CT and MRI liver lesions.
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DOI：
10.1016/j.media.2017.01.002
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发表时间：
2017-04
影响因子：
10.9
通讯作者：
Rubin DL
中科院分区：
文献类型：
--
作者：
Hoogi A;Beaulieu CF;Cunha GM;Heba E;Sirlin CB;Napel S;Rubin DL
提出了一种改进水平集分割技术的新方法——自适应局部窗口。对每个轮廓点、分割过程的迭代和每个单独的对象分别估计窗口。该方法考虑了物体尺度、空间纹理和能量函数随迭代的变化。通过计算多个灰度共生矩阵来考虑全局和局部统计量。我们展示了该方法在医学成像领域的能力，用于分割233张肝脏病变图像。为了说明我们方法的强度，这些病变通过计算机断层扫描或磁共振成像进行筛选。此外，我们使用三种不同的能量模型分析图像。我们将我们的方法与全局水平集分割、使用预定义固定大小方形窗口的局部框架和局部区域可扩展拟合模型进行了比较。结果表明，本文提出的方法在与手工标记的一致性和对轮廓初始化或使用的能量模型的依赖性方面优于其他方法。对于复杂病变，如低对比度病变、异质病变或具有嘈杂背景的病变，与目前固定大小的局部窗口相比，我们的方法显示出更好的分割效果，Dice相似系数提高了0.25±0.13 （Wilcoxon, p < 0.001）。
We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions. To illustrate the strength of our method, those lesions were screened by either Computed Tomography or Magnetic Resonance Imaging. Moreover, we analyzed images using three different energy models. We compared our method to a global level set segmentation, to a local framework that uses predefined fixed-size square windows and to a local region-scalable fitting model. The results indicate that our proposed method outperforms the other methods in terms of agreement with the manual marking and dependence on contour initialization or the energy model used. In case of complex lesions, such as low contrast lesions, heterogeneous lesions, or lesions with a noisy background, our method shows significantly better segmentation with an improvement of 0.25 ± 0.13 in Dice similarity coefficient, compared with state of the art fixed-size local windows (Wilcoxon, p < 0.001).