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Three-Dimensional SVM with Latent Variable: Application for Detection of Lung Lesions in CT Images

  • Systems-Level Quality Improvement
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Abstract

The study aims to improve the performance of current computer-aided schemes for the detection of lung lesions, especially the low-contrast in gray density or irregular in shape. The relative position between suspected lesion and whole lung is, for the first time, added as a latent feature to enrich current Three-dimensional (3D) features such as shape, texture. Subsequently, 3D matrix patterns-based Support Vector Machine (SVM) with the latent variable, referred to as L-SVM3Dmatrix, was constructed accordingly. A CT image database containing 750 abnormal cases with 1050 lesions was used to train and evaluate several similar computer-aided detection (CAD) schemes: traditional features-based SVM (SVMfeature), 3D matrix patterns-based SVM (SVM3Dmatrix) and L-SVM3Dmatrix. The classifier performances were evaluated by computing the area under the ROC curve (AUC), using a 5-fold cross-validation. The L-SVM3Dmatrix sensitivity was 93.0 with 1.23 % percentage of False Positive (FP), the SVM3Dmatrix sensitivity was 88.4 with 1.49 % percentage of FP, and the SVMfeature sensitivity was 87.2 with 1.78 % percentage of FP. The L-SVM3Dmatrix outperformed other current lung CAD schemes, especially regarding the difficult lesions.

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References

  1. Sun S. H., Bauer C., Beichel R. Automated 3D Segmentation of Lungs with Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach. IEEE Transaction on Medical Imaging. 2012, 31(2): 449–460

    Article  Google Scholar 

  2. Suzuki K. Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: Survey. IEICE Transactions on Information and Systems. 2013, E96-D(4):772–783

    Article  Google Scholar 

  3. Mohsen K., Zohreh A., Farshad T., et al. Lung Nodule Segmentation and Recognition Using SVM Classifier and Active Contour Modeling: A Complete Intelligent System. Computers in Biology and Medicine. 2013, 43(4): 287–300

    Article  Google Scholar 

  4. Kim N., Seo J B., Lee Y. G., et al. Development of an automatic classification system for differentiation of obstructive lung disease using HRCT. Journal of Digital Imaging. 2009, 22(2):136–148

    Article  Google Scholar 

  5. Rainer J. K., Michael A., Steffen A., et al. Support Vector Machine-based Prediction of Local Tumor Control After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer. International Journal of Radiation Oncology Biology Physics. 2014, 88(3): 732–738

    Article  Google Scholar 

  6. Xu R. Hirano Y., Tachibana R., et al. A Bag-of-Features Approach to Classify Six Types of Pulmonary Textures on High-Resolution Computed Tomography. IEEE Transactions on Information and Systems. 2013, E96-D(4):845–855

    Article  Google Scholar 

  7. Maciej Z. Jakub M., Tomczak M., et al. Boosted SVM for Extracting Rules from Imbalanced Data in Application to Prediction of the Post-operative Life Expectancy in the Lung Cancer Patients. Applied Soft Computing Journal. 2014, 14:99–108

    Article  Google Scholar 

  8. He X., Sahiner B., Gallas B. D., et al. Computerized Characterization of Lung Nodule Subtlety Using Thoracic CT Images. Physics in Medicine Biology. 2014, 59(4):897–910

    Article  Google Scholar 

  9. Suzuki K. Supervised ‘lesion-enhancement’ Filter by Use of a Massive-training Artificial Neural Network (MTANN) in Computer-aided Diagnosis (CAD). Physics in Medicine and Biology. 2009, 54(18): S31-S45

    Article  Google Scholar 

  10. Suzuki K., 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 Artificial Neural Network. IEEE Transactions on Medical Imaging. 2005, 24(9):1138–1150

    Article  Google Scholar 

  11. Suzuki K., Zhang J. and Xu J. Massive-training Artificial Neural Network Coupled with Laplacian-eigenfunction-based Dimensionality Reduction for Computer-aided Detection of Polyps in CT Colonography. IEEE Transactions on Medical Imaging. 2010, 29(11): 1907–1917

    Article  Google Scholar 

  12. Wang Z., Chen S. New Least Squares Support Vector Machines based on Matrix Patterns. Neural Processing Letters. 2007, 26(1): 41–56

    Article  Google Scholar 

  13. Wang Q. Z., Wang K., Li Y., et al. 3D Matrix Pattern based Support Vector Machines for Identifying Pulmonary Cancer in CT Scanned Images. Journal of Medical Systems. 2012, 36(3):1223–1228

    Article  Google Scholar 

  14. Wang Q. Z., Kang W. W., Wu C. M., et al. Computer-aided Detection of Lung Nodules by SVM based on 3D Matrix Patterns. Clinical Imaging. 2013, 37(1): 62–69

    Article  Google Scholar 

  15. Felzenszwalb P. F., Girshick R. B., McAllester D., et al. Object Detection with Discriminatively Trained Part-based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010, 32(9): 1627–1645

    Article  Google Scholar 

  16. Yang K., Wang M., Hua X. S., et al. Assemble New Object Detector with Few Examples. IEEE Transactions on Image Processing. 2011, 20(12): 3341–3349

    Article  MathSciNet  Google Scholar 

  17. Lu C. H., Zhu Z. M. and Gu X. F. An Intelligent System for Lung Cancer Diagnosis Using a New Genetic Algorithm Based Feature Selection Method. Journal of Medical Systems. 2014:38(9):1–9

    Article  Google Scholar 

  18. Chao C. M., Yu Y. W., Cheng B. W., et al. Construction the Model on the Breast Cancer Survival Analysis Use Support Vector Machine, Logistic Regression and Decision Tree. Journal of Medical Systems. 2014:38(10):106–112

    Article  Google Scholar 

  19. Mofrad F. B., Zoroofi R. A., Tehrani F. A., et al. Classification of Normal and Diseased Liver Shapes based on Spherical Harmonics Coefficients. Journal of Medical Systems. 2014:38(5):20–28

    Article  Google Scholar 

  20. Chen M. Y. and Chou C. H. Applying Cybernetic Technology to Diagnose Human Pulmonary Sounds. Journal of Medical Systems. 2014,38(6): 58–67

    Article  MathSciNet  Google Scholar 

  21. Ocak H. A Medical Decision Support System based on Support Vector Machines and the Genetic Algorithm for the Evaluation of Fetal Well-Being. Journal of Medical Systems. 2013,37(2):9913–9921

    Article  Google Scholar 

  22. Ahmet T., Niyazi K. and Aydin A. Classification of Pulmonary Nodules by Using Hybrid Features. Computational and Mathematical Methods in Medicine. 2013:1–11

  23. Cao P., Yang J. Z., Zhao D., et al. Ensemble-based Hybrid Probabilistic Sampling for Imbalanced Data Learning in Lung Nodule CAD. Computerized Medical Imaging and Graphics. 2014, 38(3): 137–150

    Article  Google Scholar 

  24. Cootes T. F., Edwards G. J., and Taylor C. J. Active Appearance Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001, 23(6): 681–685

    Article  Google Scholar 

  25. Chen X. C., Udupa J. K., Bagci U., et al. Medical Image Segmentation by Combing Graph Cuts and Oriented Active Appearance Models. IEEE Transactions on Imaging Processing. 2012, 21(4): 2035–2046

    Article  MathSciNet  Google Scholar 

  26. Vannieuwenhoven N., Vandebril R., and Meerbergen K. A New Truncation Strategy for the Higher-order Singular Value Decomposition. Siam Journal on Scientific Computing. 2012, 34, (2): 1027–1052

    Article  MathSciNet  Google Scholar 

  27. Lieven D. L., et al. A Multilinear Singular Value Decomposition. Siam Journal on Matrix Analysis and Application. 2000, 21 (4): 1253–1278

    Article  MATH  Google Scholar 

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61301257), Science and Technology Development Plan of the Jilin Province (201201107) and the Doctoral Scientific Research Fund of Northeast Dianli University (bsjxm-201104).

Conflict of interest

Qingzhu Wang, Wenchao Zhu, and Bin Wang declare that they have no conflict of interest.

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Authors and Affiliations

  1. School of Information Engineering, Northeast Dianli University, Jilin, 132012, China

    Qingzhu Wang & Wenchao Zhu

  2. Jilin Tumor Hospital, Changchun, China

    Bin Wang

Authors
  1. Qingzhu Wang
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  2. Wenchao Zhu
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  3. Bin Wang
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Corresponding author

Correspondence to Qingzhu Wang.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Wang, Q., Zhu, W. & Wang, B. Three-Dimensional SVM with Latent Variable: Application for Detection of Lung Lesions in CT Images. J Med Syst 39, 171 (2015). https://doi.org/10.1007/s10916-014-0171-5

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  • DOI: https://doi.org/10.1007/s10916-014-0171-5

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