A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets
A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets
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DOI：
10.1109/tip.2013.2282899
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发表时间：
2013-12-01
影响因子：
10.6
通讯作者：
Farag, Aly A.
中科院分区：
文献类型：
--
作者：
Farag, Amal A.;Abd El Munim, Hossam E.;Farag, Aly A.
提出了一种新的变分水平集方法用于肺CT图像中肺结节的分割。提出了一种基于隐式空间的一般肺结节形状模型。在变分分割框架中，形状模型与图像强度统计信息相融合。结节形状模型被映射到图像域的全局变换，包括不均匀的尺度，旋转和平移参数。采用形状模型和图像隐式表示之间的匹配准则来处理对齐过程。变换参数通过梯度下降优化来演变，以处理形状对准过程，从而标记结节“头部”的边界。“嵌入过程考虑了图像强度以及先前的形状信息。采用非参数密度估计方法来处理结节和背景区域的统计强度表示。所提出的技术不依赖于结节类型或位置。详尽的实验和验证结果证明从四个不同的CT肺数据库获得的742个结节，说明该方法的鲁棒性。
A new variational level set approach is proposed for lung nodule segmentation in lung CT scans. A general lung nodule shape model is proposed using implicit spaces as a signed distance function. The shape model is fused with the image intensity statistical information in a variational segmentation framework. The nodule shape model is mapped to the image domain by a global transformation that includes inhomogeneous scales, rotation, and translation parameters. A matching criteria between the shape model and the image implicit representations is employed to handle the alignment process. Transformation parameters evolve through gradient descent optimization to handle the shape alignment process and hence mark the boundaries of the nodule "head." The embedding process takes into consideration the image intensity as well as prior shape information. A nonparametric density estimation approach is employed to handle the statistical intensity representation of the nodule and background regions. The proposed technique does not depend on nodule type or location. Exhaustive experimental and validation results are demonstrated on 742 nodules obtained from four different CT lung databases, illustrating the robustness of the approach.