Delineation of FDG-PET tumors from heterogeneous background using spectral clustering.
Delineation of FDG-PET tumors from heterogeneous background using spectral clustering.
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DOI：
10.1016/j.ejrad.2012.01.001
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发表时间：
2012-11
影响因子：
3.3
通讯作者：
Grigsby, Perry W.
中科院分区：
文献类型：
--
作者：
Yang, Fei;Grigsby, Perry W.
本文探讨了在异质背景下利用光谱聚类对FDG-PET肿瘤进行分割的可行性。光谱聚类是一类利用相似矩阵的特征结构将图像体素划分为不相交聚类的聚类方法。通过捕获局部统计信息的体素变化因子缩放强度距离来衡量两个体素之间的相似性，并通过旋转特征向量矩阵来推断簇的数量，以获得最大的稀疏表示。用于评价分割精度的指标包括：骰子系数、Jaccard系数、假阳性骰子、假阴性骰子、对称平均绝对表面距离、绝对体积差。将该方法与自适应阈值分割方法在PET模拟数据上的分割结果进行了比较，结果表明，自适应阈值分割方法总体上具有更好的检测精度。将所提出的方法应用于患者数据的分割结果与医生手册注释相当吻合。这些结果表明，所提出的方法有可能准确地描绘复杂形状的FDG-PET肿瘤，这些肿瘤在异质背景下含有不均匀的活性。
This paper explored the feasibility of using spectral clustering to segment FDG-PET tumor in the presence of heterogeneous background. Spectral clustering refers to a class of clustering methods which employ the eigenstructure of a similarity matrix to partition image voxels into disjoint clusters. The similarity between two voxels was measured with the intensity distance scaled by voxel-varying factors capturing local statistics and the number of clusters was inferred based on rotating the eigenvector matrix for the maximally sparse representation. Metrics used to evaluate the segmentation accuracy included: Dice coefficient, Jaccard coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Comparison of segmentation results between the presented method and the adaptive thresholding method on the simulated PET data shows the former attains an overall better detection accuracy. Applying the presented method on patient data gave segmentation results in fairly good agreement with physician manual annotations. These results indicate that the presented method have the potential to accurately delineate complex shaped FDG-PET tumors containing inhomogeneous activities in the presence of heterogeneous background.
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