机构：[1]School of Public Health and Family Medicine, Capital Medical University, Beijing, 100069, P.R.China[2]Department of Radiology, Friendship Hospital, Capital Medical University, Beijing 100050, China[3]Department of Radiology, Xuan Wu Hospital, Capital Medical University, Beijing 100053, China放射科首都医科大学宣武医院[4]College of Business and Administration, Capital University of Economics and Business, Beijing 100070, China
To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.
基金：
The program of Natural Science Fund of China (Serial Number: 30972550);
the program of Natural Science Fund of Beijing (Serial Number: 7092010);
the program of Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality(Serial Number: PHR201007112）
语种：
外文
第一作者：
第一作者机构：[1]School of Public Health and Family Medicine, Capital Medical University, Beijing, 100069, P.R.China
通讯作者：
通讯机构：[1]School of Public Health and Family Medicine, Capital Medical University, Beijing, 100069, P.R.China
推荐引用方式(GB/T 7714)：
Guo Xiuhua,Sun Tao,Wu Haifeng,et al.Support vector machine prediction model of early-stage lung cancer based on curvelet transform to extract texture features of CT image[J].World Academy of Science, Engineering and Technology.2010,71:
APA：
Guo Xiuhua,Sun Tao,Wu Haifeng,He Wen,Liang Zhigang...&Wang Wei.(2010).Support vector machine prediction model of early-stage lung cancer based on curvelet transform to extract texture features of CT image.World Academy of Science, Engineering and Technology,71,
MLA：
Guo Xiuhua,et al."Support vector machine prediction model of early-stage lung cancer based on curvelet transform to extract texture features of CT image".World Academy of Science, Engineering and Technology 71.(2010)
