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中国生物医学工程学报  2017, Vol. 36 Issue (1): 12-19    DOI: 10.3969/j.issn.0258-8021. 2017. 01.002
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基于Local Jet变换空间纹理特征的肺结节分类方法
代美玲1, 祁 瑾1,3, 周仲兴1,2, 高 峰1,2#*
1(天津大学精密仪器与光电子工程学院,天津 300072)
2(天津市生物医学检测技术与仪器重点实验室, 天津 300072)
3(天津医科大学附属肿瘤医院, 天津 300060)
The Classification of Pulmonary Nodules Based on Texture Features over Local Jet Transformation Space
Dai Meiling1, Qi Jin1,3, Zhou Zhongxing1,2, Gao Feng 1,2#*
1 (College of Precision Instrument and Optoelectronics Engineering,Tianjin University,Tianjin 300072,China)
2(Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments,Tianjin 300072,China)
3(Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China)
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摘要 为了在纹理特征下改善肺结节良、恶性的模式识别,提出一种基于local jet变换空间的纹理特征提取方法。首先利用二维高斯函数的前三阶偏微分函数将结节原图像变换到local jet纹理图像空间,然后利用纹理描述子在该空间提取特征参数。以灰度值的前四阶矩和基于灰度共生矩阵的特征参数作为纹理描述子,分别提取结节原图像和变换后纹理图像的特征参数,以BP神经网络作为分类器,对同一纹理描述子下的2个不同图像空间的经核主成分分析优化后的特征参数集进行结节良、恶性分类。以157个肺结节(51个良性,106个恶性)作为实验数据进行对比实验,结果显示:两种纹理描述子基于local jet变换空间提取的特征参数分别获得82.69%和86.54%的分类正确率,较原图像空间提高6%~8%,同时AUC值提高约10%。实验结果表明,基于local jet变换空间提取的纹理特征可以有效地改善肺结节良、恶性的模式识别。
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代美玲
祁 瑾
周仲兴
高 峰
关键词 肺结节 local jet空间变换 纹理特征 模式识别    
Abstract:A method of texture features extraction over local jet transformation space is presented in order to improve the recognition about benign/malignant nodules with texture features. In this method, the first three order partial differential functions of 2-D Gaussian were used to transform original image of nodule tolocal jet texture images space firstly, and then feature parameters were extracted over the local jet images space by texture descriptor. In our work, feature parameters were extracted from the original CT image and local jet images using the texture descriptors of first four moments of intensity and parameters based on gray level co-occurrence matrix. Then BP neural network was used to classify the benign/malignant nodules with the two texture features sets under the same texture descriptor that optimized by kernel principle component analysis. The compared experiment conducted based on 157 lung nodule(51 benign and 106 malignant) samples. The experiment result showed that the classification accuracy of two feature parameters sets extracted by the two texture descriptors on local jet space was 82.69% and 86.54% respectively, higher 6%~8% than feature parameters sets on original image space, and the value of AUC was increased about 10%. The experiment result indicated the texture features extracted from local jet transformation space could effectively improve the pattern recognition of benign/malignant of pulmonary nodules.
Key wordspulmonary nodule    local jet space transformation    texture features    pattern recognition
收稿日期: 2016-04-22     
PACS:  R318  
通讯作者: E-mail: gaofeng@tju.edu.cn   
作者简介: # 中国生物医学工程学会高级会员
引用本文:   
代美玲, 祁 瑾, 周仲兴, 高 峰. 基于Local Jet变换空间纹理特征的肺结节分类方法[J]. 中国生物医学工程学报, 2017, 36(1): 12-19.
Dai Meiling, Qi Jin, Zhou Zhongxing, Gao Feng. The Classification of Pulmonary Nodules Based on Texture Features over Local Jet Transformation Space. Chinese Journal of Biomedical Engineering, 2017, 36(1): 12-19.
链接本文:  
http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021. 2017. 01.002     或     http://cjbme.csbme.org/CN/Y2017/V36/I1/12
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