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首页> 外文会议>BMEI 2012;International Conference on Biomedical Engineering and Informatics >A segmentation method for sub-solid pulmonary nodules based on fuzzy c-means clustering
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A segmentation method for sub-solid pulmonary nodules based on fuzzy c-means clustering

机译：基于模糊c均值聚类的亚实心肺结节分割方法

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摘要

Accurately and reliably automated segmentation of pulmonary tumors could play an important role in lung cancer diagnosis and radiation oncology work. However, it remains a very difficult task in particular for segmenting pulmonary tumors associated with sub-solid nodules that are partially obscured in lung CT images. In this study, we proposed and tested an improved weighed kernel fuzzy c-means (IWKFCM) method that incorporates vessels structure information and classes' distribution as weights to segment sub-solid pulmonary nodules. For this purpose, a ROI of a nodule in center CT slice is manually defined. The IWKFCM algorithm is applied to identify and cluster the potential nodule pixels located in this manually-defined center slice and its adjacent slices. The sub-solid nodule is then segmented and defined through 3D connected component labeling and morphological post-processing. The segmentation method was tested using a public CT dataset (LIDC) including 36 nodules. The average overlap ratio between the automated and radiologists' segmentation of nodules is 76.18%. The false-positive ratio (FPR) and false-negative ratio (FNR) are smaller. Experimental results showed that the proposed method enabled to achieve more accurate result in segmenting sub-solid pulmonary nodules.
机译：准确可靠地自动分割肺部肿瘤可能在肺癌诊断和放射肿瘤学工作中发挥重要作用。然而，尤其对于分割与在肺部CT图像中被部分掩盖的亚实性结节相关的肺部肿瘤，这仍然是非常困难的任务。在这项研究中，我们提出并测试了一种改进的加权核模糊c均值（IWKFCM）方法，该方法结合了血管结构信息和类别的分布作为权重来分割亚实心肺结节。为此，手动定义中心CT切片中结节的ROI。 IWKFCM算法用于识别和聚类位于此手动定义的中心切片及其相邻切片中的潜在结节像素。然后，通过3D连接的组件标记和形态学后处理对亚固体小节进行分割和定义。使用包括36个结节的公共CT数据集（LIDC）测试了分割方法。自动化和放射科医生对结节的分割之间的平均重叠率为76.18％。假阳性比率（FPR）和假阴性比率（FNR）较小。实验结果表明，该方法在分割亚实心肺结节方面取得了较准确的结果。

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