|本期目录/Table of Contents|

[1]庞学明,张泽伟,侯爱林,等.基于支持向量机与随机游走结合的GGO型肺结节分割方法[J].天津医科大学学报,2018,24(03):263-266.
　PANG Xue-ming,ZHANG Ze-wei,HOU Ai-lin,et al.GGO pulmonary nodule segmentation method based on support vector machine and random walk[J].Journal of Tianjin Medical University,2018,24(03):263-266.
点击复制

基于支持向量机与随机游走结合的GGO型肺结节分割方法(PDF)
分享到：

《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:
24卷
期数:
2018年03期
页码:
263-266
栏目:
技术与方法
出版日期:
2018-05-20

文章信息/Info

Title:
GGO pulmonary nodule segmentation method based on support vector machine and random walk
作者:
庞学明1; 张泽伟2; 侯爱林2; 孙浩然1
1.天津医科大学总医院放射科，天津 300052；2.天津医科大学医学影像学院，天津 300203
Author(s):
PANG Xue-ming1; ZHANG Ze-wei2; HOU Ai-lin2; SUN Hao-ran1
1.Department of Radiology,General Hospital,Tianjin Medical University，Tianjin 300052,China; 2.School of Medical Imaging, Tianjin Medical University,Tianjin 300203,China
关键词:
GGO型肺结节; 支持向量机; 随机游走; 图像分割
Keywords:
GGO pulmonary nodules; support vector machine; random walk; image segmentation
分类号:
R445
DOI:
-
文献标志码:
A
摘要:
目的：为了提高磨玻璃型肺结节（GGO型肺结节）的分割精度，提出一种基于支持向量机与随机游走相结合的分割方法。方法：利用已手动分割的GGO型肺结节训练支持向量机。由训练后的分类模型在待分割的GGO型肺结节图像中选择种子点，然后利用随机游走算法根据支持向量机选取的种子点进行GGO型肺结节图像分割。结果：该研究纳入150个待分割GGO型结节图像，上述分割算法的平均准确率为98.05%、平均召回率为96.35%和平均F1值为98.05%。与传统方法相比，本方法实现了GGO型肺结节的精确自动化分割，对GGO型肺结节定量、定性分析提供更加准确的客观依据。结论：该方法利用支持向量机选取种子点，并利用随机游走进行结节分割可以有效地对GGO型肺结节进行分割，具有简单高效，准确率高的优点。
Abstract:
Objective: To improve the segmentation precision of the ground glass opacity(GGO) type pulmonary nodules, by a segmentation method based on the combination of support vector machine and random walk. Methods: GGO pulmonary nodules after manual segmentation were used to train support vector machines. The trained classification model was used to select seed points on the image of GGO pulmonary nodules that need to be segmented. The random walk algorithm segmented was applied to segment the GGO pulmonary nodules based on the seed points selected by the support vector machine. Results: The study included 150 GGO pulmonary nodules to be segmented. The average accuracy rate of the above algorithm was 98.05%, the average recall rate was 96.35% and the average F1 value was 98.05%. Compared with the traditional method, the algorithm could achieve accurate segmentation of GGO pulmonary nodules and provide more accurate and objective basis for the quantitative and qualitative analysis of GGO pulmonary nodules. Conclusion：The method relies on support vector machines to select seed points and random walk to segment . This method can effectively segment GGO lung nodules, and has the advantages of simplicity, high efficiency and high accuracy.

参考文献/References:

[1] Siegel R L, Miller K D, Jemal A. Cancer statistics, 2016[J]. CA Cancer J Clin,2016,66(1):7
[2] 韦春晖. 肺癌早期诊断进展[J].临床肺科杂志,2010,15(8):1136
[3] Cortes C,VapnikV. Support-Vector networks[J]. Mach Learn,1995, 20(3):273
[4] Fasshauer G E, Hickernell F J,Ye Q. Solving support vector machines in reproducing kernel Banach spaces with positive definite functions[J]. Appl Comput Harmon Anal,2015,38(1):115
[5] Gillies R J, Kinahan P E, Hricak H. Radiomics. Images are more than pictures, they are data[J]. Radiology, 2016,278(2):563
[6] Grady L. Random walks for image segmentation[J]. IEEE Trans Pattern Anal Mach Intell,2006,28(11):1768
[7] DehmeshkiJ, AminH, Valdivieso M, et al. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach[J]. IEEE Trans Med Imaging,2008,27(4):467
[8] 刘慧,张彩明,邓凯,等.改进局部自适应的快速FCM肺结节分割方法[J].计算机辅助设计与图形学学报,2014,26(10):1727
[9] Way T W, Hadjiiski L M, Sahiner B, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours[J]. Med Phys,2006,33(7):2323
[10] Son W, Park S J, P ark C M, et al. Automated volumetric segmentation method for computerized-diagnosis of pure nodular ground-glass opacity in high-resolution CT[C]. Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241P
[11] Zhou J, Chang S, Metaxas D N, et al. An automatic method for ground glass opacity nodule detection and segmentation from CT studies[J]. ConfProc IEEE Eng Med BiolSoc, 2006, 1:3062
[12] Zhang L, Fang M, Novak C L, et al. Consistent interactive segmentation of pulmonary ground glass nodules identified in CT studies[C]. Proceedings of SPIE,2004, 5370:1709

相似文献/References:

备注/Memo

备注/Memo:
文章编号 1006-8147(2018)03-0263-04
基金项目 天津医科大学科学基金资助项目（2110/2YX011）
作者简介 庞学明（1986-），男，硕士在读，研究方向：影像医学与核医学；孙浩然，E-mail:sunhaoran2006@hotmail.com。
更新日期/Last Update: 2018-05-20