- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 148363, 11 pages
Classification of Pulmonary Nodules by Using Hybrid Features
1Department of Engineering Sciences, Istanbul University, 34320 Avcılar, Istanbul, Turkey
2Department of Electrical and Electronics Engineering, Istanbul University, 34320 Avcılar, Istanbul, Turkey
Received 28 March 2013; Revised 24 May 2013; Accepted 29 May 2013
Academic Editor: Chung-Ming Chen
Copyright © 2013 Ahmet Tartar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- K. Doi, “Computer-aided diagnosis in medical imaging: historical review, current status and future potential,” Computerized Medical Imaging and Graphics, vol. 31, no. 4-5, pp. 198–211, 2007.
- M. L. Giger, H.-P. Chan, and J. Boone, “Anniversary paper: history and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM,” Medical Physics, vol. 35, no. 12, pp. 5799–5820, 2008.
- R. M. Summers, “Road maps for advancement of radiologic computer-aided detection in the 21st century,” Radiology, vol. 229, no. 1, pp. 11–13, 2003.
- “Cancer facts and figs,” The American Cancer Society, 2009.
- A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, and M. J. Thun, “Cancer statistics, 2009,” CA Cancer Journal for Clinicians, vol. 59, no. 4, pp. 225–249, 2009.
- K. Kanazawa, Y. Kawata, N. Niki et al., “Computer-aided diagnosis for pulmonary nodules based on helical CT images,” Computerized Medical Imaging and Graphics, vol. 22, no. 2, pp. 157–167, 1998.
- U. Baĝci, M. Bray, J. Caban, J. Yao, and D. J. Mollura, “Computer-assisted detection of infectious lung diseases: a review,” Computerized Medical Imaging and Graphics, vol. 36, no. 1, pp. 72–84, 2012.
- K. Murphy, B. van Ginneken, A. M. R. Schilham, B. J. de Hoop, H. A. Gietema, and M. Prokop, “A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification,” Medical Image Analysis, vol. 13, no. 5, pp. 757–770, 2009.
- S. Iwano, T. Nakamura, Y. Kamioka, and T. Ishigaki, “Computer-aided diagnosis: a shape classification of pulmonary nodules imaged by high-resolution CT,” Computerized Medical Imaging and Graphics, vol. 29, no. 7, pp. 565–570, 2005.
- S. Iwano, T. Nakamura, Y. Kamioka, M. Ikeda, and T. Ishigaki, “Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT,” Computerized Medical Imaging and Graphics, vol. 32, no. 5, pp. 416–422, 2008.
- H. Chen, J. Zhang, Y. Xu, B. Chen, and K. Zhang, “Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans,” Expert Systems with Applications, vol. 39, pp. 11503–11509, 2012.
- T. Kubota, A. K. Jerebko, M. Dewan, M. Salganicoff, and A. Krishnan, “Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models,” Medical Image Analysis, vol. 15, no. 1, pp. 133–154, 2011.
- D.-T. Lin, C.-R. Yan, and W.-T. Chen, “Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system,” Computerized Medical Imaging and Graphics, vol. 29, no. 6, pp. 447–458, 2005.
- A. Retico, P. Delogu, M. E. Fantacci, I. Gori, and A. Preite Martinez, “Lung nodule detection in low-dose and thin-slice computed tomography,” Computers in Biology and Medicine, vol. 38, no. 4, pp. 525–534, 2008.
- T. Messay, R. C. Hardie, and S. K. Rogers, “A new computationally efficient CAD system for pulmonary nodule detection in CT imagery,” Medical Image Analysis, vol. 14, no. 3, pp. 390–406, 2010.
- R. C. Hardie, S. K. Rogers, T. Wilson, and A. Rogers, “Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs,” Medical Image Analysis, vol. 12, no. 3, pp. 240–258, 2008.
- J. J. Suárez-Cuenca, P. G. Tahoces, M. Souto et al., “Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images,” Computers in Biology and Medicine, vol. 39, no. 10, pp. 921–933, 2009.
- M. Hanamiya, T. Aoki, Y. Yamashita, S. Kawanami, and Y. Korogi, “Frequency and significance of pulmonary nodules on thin-section CT in patients with extrapulmonary malignant neoplasms,” European Journal of Radiology, vol. 81, no. 1, pp. 152–157, 2012.
- M. C. Lee, L. Boroczky, K. Sungur-Stasik et al., “Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction,” Artificial Intelligence in Medicine, vol. 50, no. 1, pp. 43–53, 2010.
- W.-J. Choi and T.-S. Choi, “Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images,” Information Sciences, vol. 212, pp. 57–78, 2012.
- Q. Wang, W. Kang, C. Wu, and B. Wang, “Computer-aided detection of lung nodules by SVM based on 3D matrix patterns,” Clinical Imaging, vol. 37, no. 1, pp. 62–69, 2013.
- S. L. A. Lee, A. Z. Kouzani, and E. J. Hu, “Random forest based lung nodule classification aided by clustering,” Computerized Medical Imaging and Graphics, vol. 34, no. 7, pp. 535–542, 2010.
- A. Bosch, A. Zisserman, and X. Muñoz, “Image classification using random forests and ferns,” in Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV '07), Rio de Janeiro, Brazil, October 2007.
- E. Bauer and R. Kohavi, “An empirical comparison of voting classification algorithms: bagging, boosting, and variants,” Machine Learning, vol. 36, no. 1-2, pp. 105–139, 1999.
- R. Maclin and D. Opitz, “Empirical evaluation of bagging and boosting,” in Proceedings of the 14th National Conference on Artificial Intelligence (AAAI '97), pp. 546–551, July 1997.
- T.-K. An and M.-H. Kim, “A new diverse AdaBoost classifier,” in Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence (AICI '10), pp. 359–363, Sanya, China, October 2010.
- Z. Zhang and X. Xie, “Research on AdaBoost.M1 with random forest,” in Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET '10), vol. 1, pp. 647–652, Chengdu, China, April 2010.
- A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: a review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4–37, 2000.
- L. J. Hargrove, G. Li, K. B. Englehart, and B. S. Hudgins, “Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 5, pp. 1407–1414, 2009.
- H. Kong, L. Wang, E. K. Teoh, X. Li, J.-G. Wang, and R. Venkateswarlu, “Generalized 2D principal component analysis for face image representation and recognition,” Neural Networks, vol. 18, no. 5-6, pp. 585–594, 2005.
- R. Gonzales and R. Woods, Image Processing, Prentice Hall, New York, NY, USA, 2007.
- R. Opfer and R. Wiemker, “Performance analysis for computer-aided lung nodule detection on LIDC data,” in Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, vol. 6515 of Proceedings of the SPIE, San Diego, Calif, USA, 2007.
- G. D. Rubin, J. K. Lyo, D. S. Paik et al., “Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection,” Radiology, vol. 234, no. 1, pp. 274–283, 2005.
- B. Sahiner, L. M. Hadjiiski, H.-P. Chan et al., “Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: observer performance study,” in Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, vol. 6515 of Proceedings of SPIE, San Diego, Calif, USA, 2007.
- K. Suzuki, S. G. Armato III, F. Li, S. Sone, and K. Doi, “Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography,” Medical Physics, vol. 30, no. 7, pp. 1602–1617, 2003.
- A. Tartar, N. Kılıç, and A. Akan, “Bagging support vector machine approaches for pulmonary nodule detection,” in Proceedings of the International Conference on Control, Decision and Information Technologies, Tunisia, May 2013.
- C. Solomon and T. Breckon, Fundamentals of Digital Image Processing: A Practical Approach With Examples in Matlab, Wiley-Blackwell, 2011.
- H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005.
- L. Lancashire, S. Ugurel, C. Creaser, D. Schadendorf, R. Rees, and G. Ball, “Utilizing artificial neural networks to elucidate serum biomarker patterns which discriminate between clinical stages in melanoma,” in Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB '05), La Jolla, Calif, USA, November 2005.
- H. I. Erdal, O. Karakurt, and E. Namli, “High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform,” Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp. 1246–1254, 2013.
- I. Mukherjee and S. Routroy, “Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process,” Expert Systems with Applications, vol. 39, no. 3, pp. 2397–2407, 2012.
- L. Breiman, “Random forests,” Tech. Rep., Statistics Department, University of California, Berkeley, Calif, USA, 1999.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
- L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.
- D. Opitz and R. Maclin, “Popular ensemble methods: an empirical study,” Journal of Artificial Intelligence Research, vol. 11, pp. 169–198, 1999.
- H. Fan and H. Wang, “Preditcing protein subcellular location by AdaBoost.M1 algorithm,” in Proceedings of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC '11), pp. 3168–3171, Deng Leng, China, August 2011.
- Y. Freund and R. E. Schapire, “Experiments with a new boosting algorithm,” in Proceedings of the 13th Conference on Machine Learning, pp. 148–156, 1996.
- Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
- B. Li, K. Chen, L. Tian, Y. Yeboah, and S. Ou, “Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 515386, 15 pages, 2013.
- T. Fawett, “ROC graphs: notes and practical considerations for data mining researches,” Tech. Rep. HPL-2003-4, HP Labs, 2003.
- J. Shiraishi, Q. Li, K. Suzuki, R. Engelmann, and K. Doi, “Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification,” Medical Physics, vol. 33, no. 7, pp. 2642–2653, 2006.