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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型聚类的亚固态肺结节的分段方法

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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：准确且可靠地自动化肺肿瘤的分割可能在肺癌诊断和放射肿瘤学工作中发挥重要作用。然而，对于分割与肺CT图像部分模糊的副固结节相关的分割肺肿瘤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称重核模糊C型方法（IWKFCM）方法，该方法将血管结构信息和类分配作为重量分配给分段亚固体肺结核。为此目的，手动定义中心CT切片中的结节的ROI。应用IWKFCM算法以识别和培养位于该手动定义的中心切片中的潜在结节像素及其相邻的切片。然后通过3D连接的部件标记和形态后处理分段和定义亚固结节。使用包括36个结节的公共CT数据集（LIDC）测试分割方法。结节的自动化和放射科学师分割之间的平均重叠比率为76.18％。假阳性比（FPR）和假阴性比（FNR）较小。实验结果表明，该方法使得能够在分段亚固肺结节中实现更准确的结果。

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