Texture analysis improves level set segmentation of the anterior abdominal wall.
Texture analysis improves level set segmentation of the anterior abdominal wall.
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DOI：
10.1118/1.4828791
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发表时间：
2013-12
期刊：
影响因子：
3.8
通讯作者：
Zhoubing Xu;W. M. Allen;R. Baucom;B. Poulose;B. Landman
中科院分区：
文献类型：
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作者：
Zhoubing Xu;W. M. Allen;R. Baucom;B. Poulose;B. Landman
腹疝的治疗一直是医疗保健中的一个挑战性问题。这些疝的修复充满了失败;据报道，即使使用生物相容性补片，复发率也在24%至43%之间。目前，计算机断层扫描（CT）用于通过专家指导干预，但没有使用定性的临床判断，特别是基于图像处理的定量指标。作者提出，捕获腹壁三维结构及其异常的图像分割方法将为测量疝和周围组织的几何特性提供基础，从而优化干预。方法在这项研究中，20例临床获得的术后患者的CT扫描，作者展示了一种新的方法，腹部几何分类。作者的方法使用基于Gabor滤波器的纹理分析来提取特征向量，并遵循模糊c均值聚类方法来估计八个聚类的体素概率成员。从纹理分析中估计的隶属度有助于识别具有不均匀强度的解剖结构。该隶属度用于指导水平集演化，以及导出接近腹壁的初始开始。结果腹壁分割结果的定量和定性验证与表面误差的基础上手动标记的地面真理。使用纹理，腹壁外表面的平均表面误差小于2 mm，91%的外表面距离手动描记小于5 mm;未使用纹理的方法的误差显著更大（2-5 mm）。结论：作者的方法为改善VH护理建立了一个表征腹壁的基线。CT图像中固有的纹理模式有助于组织分类，纹理分析可以改善腹部周围区域的水平集分割。
PURPOSE The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. METHODS In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors' approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall. RESULTS Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2-5 mm) for methods that did not use the texture. CONCLUSIONS The authors' approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.