中文摘要：

肺癌是当今对人类健康与生命危害最大的恶性肿瘤之一。早期肺癌一般表现为肺结节,如能及时从肺部CT图像中检测到肺结节,便能及早发现肺癌,经治疗后可有效延长患者的生存时间,所以CT图像是肺癌诊断和疾病治疗的重要依据。但对全肺进行螺旋CT扫描产生的大量图像给人工检测肺结节带来了困难,因此,基于CT图像的肺结节计算机辅助检测（CAD）技术应运而生。由于CAD能有效辅助放射科医生提高肺结节的检测准确率与工作效率,降低漏诊与误诊率,因此,CAD成了目前生物医学工程领域的研究热点之一。尽管目前报道的CAD系统所采用的方法各有不同,但基本上都是遵循以下步骤完成：1）CT图像的预处理;2）肺结节的分割;3）特征提取及优化选择;4）肺结节的分类识别。其中对结节的精确分割与否直接影响到后续的特征选择与优化,而特征选择与优化又进而影响到分类器的分类属性,所以肺结节分割是基于CT图像的肺结节计算机辅助检测的关键步骤。 肺结节可细分为实质性结节（solid nodule）和亚实质性结节（sub-solid nodule）。其中完全屏蔽肺实质的结节称为实质性结节,否则称为亚实质性结节。实质性结节表现为边界比较规则的类圆形病灶,且密度较高、边界清晰,因此较容易分割,对实质性肺结节的分割国内外均有大量文献报道。与实质性肺结节相比,亚实质性肺结节其密度表现为磨玻璃影（GGO）,且边缘不清晰（多带毛刺）、没有特定的形状。实质性结节中93%以上为良性病灶,而因为带有GGO,亚实质性肺结节的恶性化程度较实质性结节而言表现得较高。因此,亚实质性结节的精确分割对发现早期肺癌更具应用价值,也面临更大的难度和挑战。 模糊聚类算法是一种基于模糊数学的常用的灰度图像分割方法,适合解决灰度图像中存在的模糊和不确定性问题。

英文摘要：

Accurately and reliably automated segmentation of pulmonary tumors could play an important role in lung cancer diagnosis and radiation oncology. 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 propose and test 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 region of interest （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 （surrounding） slices. The sub-solid nodule is then segmented and defined through 3D connected component labeling and morphological post-processing. This segmentation method is tested using two datasets including 36 nodules selected from a public dataset （LIDC） and 18 nodules depicted on CT images collected from our local hospital. The average overlap ratios between the automated and radiologists’ segmentation of nodules of two datasets are 76.18% and 71.65% respectively. In both datasets, the false-positive ratio （FPR） and false-negative ratio （FNR） are smaller than 17%. Experimental results show that the proposed method enables us to achieve more accurate result in segmenting sub-solid pulmonary nodules than the other previously reported clustering methods. The segmentation results could also provide a consultative reference for more accurately extracting image features and optimal classification of pulmonary nodules in developing computer-aided detection （CAD） schemes.