吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (2): 508-515.doi: 10.13229/j.cnki.jdxbgxb201402037

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Multiple kernel MtLSSVM and its application in lung nodule recognition

LI Yang1,2, WEN Dun-wei3, WANG Ke1, LIU Le2   

  1. 1. College of Communication Engineering, Jilin University, Changchun 130012, China;
    2. College of Computer Science and Engineering, Changchun University of Technology, Changchun 130022, China;
    3. School of Computing and Information Systems, Athabasca University, Athabasca, Alberta T9S3A3, Canada
  • Received:2013-06-08 Online:2014-02-01 Published:2014-02-01

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Abstract:

Traditional methods for lung nodule recognition need to extract the features of the Region of Interests (ROIs), which usually leads to loss of some implicit structure information. To avoid this problem, a novel Multiple Kernel Learning method based on Matrix Least Square Support Vector Machine (MKL-MatLSSVM) is proposed. This method combines the advantages of both MKL method and MatLSSVM, and supports direct matrix input, suitable for image identification. To verify the effectiveness of the proposed method, it was applied to identify lung nodules in CT images of 20 patients, where the extracted ROIs contain 80 nodules and 190 false positives. The results show that when using hybrid or Radial Basis Function (RBF) kernels in MKL-MatLSSVM, the resulting sensitivity, accuracy and specificity can be balanced, and the area under the Receiver Operating Characteristic (ROC) curve can reach 96%, better than other two previous Support Vector Machine (SVM) methods that include MatLSSVM.

Key words: information processing, image recognition, lung nodule recognition, MKL-MatLSSVM algorithm, multiple kernel learning, support vector machines

CLC Number: 

  • TP391.4

[1] Farag A, Ayman E B, Gimel F G, et al. Detection and recognition of lung nodules in spiral CT images using deformable templates and Bayesian post-classification[C]//International Conference on Image Processing(ICIP 2004), Singapore, 2004.

[2] Kenji S, Li F, Sone S, et al. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificialneural network[J]. IEEE Transactions on Medical Imaging, 2005, 24(9): 1138-1150.

[3] 孙申申, 任会之, 康雁, 等. 基于遗传算法和支持向量机的肺结节检测[J].系统仿真学报, 2011, 23(3): 497-501, 566. Sun Shen-shen, Ren Hui-zhi, Kang Yan, et al. Lung nodule detection by GA and SVM[J]. Journal of System Simulation, 2011, 23(3): 497-501, 566.

[4] 张婧, 李彬, 田联房, 等.结合规则和SVM方法的肺结节识别[J].华南理工大学学报:自然科学版, 2011, 39(2): 125-129, 147. Zhang Jing, Li Bin, Tian Lian-fang, et al. Lung nodule recognition combining rule-based method and SVM[J]. Journal of South China University of Technology (Natural Science Edition), 2011, 39(2):125-129, 147.

[5] Suzuki K, Armato S G, Li F, et al. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography[J]. Med Phys, 2003, 30(6): 1602-1617.

[6] Wang Z, Chen S C. New least squares support vector machines based on matrix patterns[J]. Neural Processing Letters, 2007, 26(1): 41-56.

[7] 王青竹. 基于三维SVMs的肺部CT中的结节检测算法[D]. 长春:吉林大学通信工程学院, 2011. Wang Qing-zhu. Detection of lung nodules in CT images based on 3D SVMs[D]. Changchun: College of Communication Engineering, Jilin University, 2011.

[8] Wang Q Z, Kang W W, Wu C M, et al. Computer-aided detection of lung nodules by SVM based on 3D matrix patterns[J]. Clinical Imaging, 2013, 37(1):62-69.

[9] 孙申申.基于CT影像的肺结节检测与分割方法研究[D]. 沈阳:东北大学信息科学与工程学院, 2009. Sun Shen-shen. Lung nodule detection and segmentation methods based on CT images[D]. Shenyang: School of Information Science & Engineering, Northeastern University, 2009.

[10] 皋军. 智能识别中的降维新方法及其应用研究[D].无锡:江南大学信息工程学院, 2010. Gao Jun. The study of novel dimensionality reduction methods and application in intelligent recognition[D]. Wuxi: School of Information Engineering, Jiangnan University, 2010.
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