Segmentation of Ground Glass Opacity Pulmonary Nodules With Sparse Representation and Random Walk
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摘要: 肺结节是早期肺癌在影像学上的表现形式.磨玻璃型（Ground glass opacity，GGO）肺结节被认为是恶变可能性最大的一类结节之一.针对GGO结节边缘模糊、大小各异、形状不规则和灰度不均匀等造成分割准确率低问题，本文提出了一种基于稀疏表示和随机游走模型的分割算法.首先，利用测地距离和局部搜索策略，自动地选取了种子点.其次，联合8-!邻域和稀疏表示的K-!最近邻算法建立了新的图，避免了噪声的干扰.结合灰度、纹理、空间距离和稀疏系数构建了新的加权矩阵.最后，将标签限制项引入到随机游走的能量函数中.该算法分割准确性较高，鲁棒性较强.Abstract: Pulmonary nodules are the radiographic manifestation of lung cancer in early stages. Ground glass opacity (GGO) pulmonary nodules are considered to be one of the most likely nodules of malignancy. To address the low accuracy segmentation problem caused by blurred boundaries, different sizes, irregular shapes and inhomogeneous intensities of GGO nodules, a segmentation algorithm with the sparse representation and random walk model is proposed. Firstly, the geodesic distance and local search strategy are introduced to automatically select seeds. Secondly, 8-neighbor and sparse representation K-nearest neighbor algorithm are combined to build a new graph which avoids the interference of image noise. To construct a new weighted matrix, intensity, texture, spatial distance and sparse coefficients are incorporated. Finally, a label constraint term is added to the energy function of random walker. The proposed algorithm can obtain a high accuracy and strong robustness.
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图 5 $T$和$\kappa$取不同值时结节种子点选取和对应的分割结果
Fig. 5 Seed selection and the corresponding segmentation results when using different $T$ and $\kappa$
图 6 不同$\lambda$时本文提出的SRRW算法分割的结果
Fig. 6 Segmentation results of the proposed SRRW algorithm with different $\lambda$
图 7 不同的结节种子点时SRRW算法的GGO结节分割
Fig. 7 GGO nodule segmentation of SRRW algorithm using different nodule seeds
图 8 RW、RWR、SubRW和SRRW对GGO结节分割结果
Fig. 8 Segmentation results of GGO nodule by using RW、RWR、SubRW and SRRW
图 10 本文提出SRRW算法对GGO结节序列图像分割
Fig. 10 Segmentation of the proposed SRRW algorithm in a CT image sequence with a GGO nodule
表 1 SRRW、RW和SubRW算法的Overlap值对比
Table 1 Comparison results of Overlap values among SRRW、RW and SubRW algorithms
CT图像 SRRW算法 RW算法 SubRW算法 LIDC-000498 0.9145 0.8534 0.8942 LIDC-000058 0.9324 0.9164 0.9632 LIDC-000125 0.8652 0.8432 0.8223 LIDC-000043 0.8942 0.9473 0.9024 LIDC-000074 0.9724 0.9636 0.9475 LIDC-000119 0.8148 0.7647 0.8086 LIDC-000063 0.9025 0.8437 0.8946 LIDC-000054 0.9438 0.9421 0.9676 LIDC-000106 0.8142 0.7642 0.8247 LIDC-000116 0.9798 0.9075 0.9248 Mean 0.9034 0.8746 0.8950 Std. 0.0583 0.0725 0.0590 
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表 2 本文的种子点选择策略的执行时间
Table 2 Execution times of seed selection strategy
序列号 执行时间(s) 序列号 执行时间(s) 1 0.2554 6 0.2054 2 0.2087 7 0.2081 3 0.2122 8 0.2225 4 0.2069 9 0.2041 5 0.2067 10 0.2060
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表 3 不同肺结节分割算法的Overlap值的对比
Table 3 Comparisons of Overlap values among difierent pulmonary nodule segmentation algorithms
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