Abstract
Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abraham, J.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011)
Parkin, D.M.: Global cancer statistics in the year 2000. Lancet Oncol. 2, 533–543 (2001)
Bach, P.B., Mirkin, J.N., Oliver, T.K., Azzoli, C.G., Berry, D., Brawley, O.W., Byers, T., Colditz, G.A., Gould, M.K., Jett, J.R.: Benefits and harms of CT screening for lung cancer: a systematic review. JAMA, J. Am. Med. Assoc. 307, 2418–2429 (2012)
Kumar, D., Wong, A., Clausi, D.A.: Lung nodule classification using deep features in CT images. Comput. Robot Vis. 327, 110–116 (2015)
Partio, M., Cramariuc, B., Gabbouj, M., Visa, A.: Rock texture retrieval using gray level co-occurrence matrix. In: Proceedings of the Nordic Signal Processing Symposium Norsig Norway (2002)
Wu, H., Sun, T., Wang, J., Li, X., Wang, W., Huo, D., Lv, P., He, W., Wang, K., Guo, X.: Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography. J. Digit. Imaging 26, 797–802 (2013)
Aggarwal, T., Furqan, A., Kalra, K.: Feature extraction and LDA based classification of lung nodules in chest CT scan images. In: 2015 International Conference on Advances in Computing, Communications and Informatics, pp. 1189–1193. IEEE Press, New York (2015)
Mabrouk, M., Karrar, A., Sharawy, A.: support vector machine based computer aided diagnosis system for large lung nodules classification. J. Med. Imaging Health Inform. 3, 214–220 (2013)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 389–396 (2011)
Anand, S.K.V.: Segmentation coupled textural feature classification for lung tumor prediction. In: 2010 IEEE International Conference on Communication Control and Computing Technologies, pp. 518–524. IEEE Press, New York (2010)
Frejlichowski, D.: An experimental comparison of seven shape descriptors in the general shape analysis problem. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6111, pp. 294–305. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13772-3_30
Sokic, E., Konjicija, S.: Shape description using phase-preserving Fourier descriptor. In: ICME 2015, pp. 1–6. IEEE Press, New York (2015)
Zhang, D., Lu, G.: Shape-based image retrieval using generic Fourier descriptor. Signal Process. Image 17, 825–848 (2002)
Utkin, L.V., Chekh, A.I., Zhuk, Y.A.: Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels. Neural Netw. Off. J. Int. Neural Netw. Soc. 80, 53–66 (2016)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Discov. 2, 121–167 (1998)
Dandil, E., Cakiroglu, M., Eksi, Z., Ozkan, M.: Artificial neural network-based classification system for lung nodules on computed tomography scans. In: Soft Computing and Pattern Recognition, pp. 382–386 (2014)
Lee, S., Kouzani, A.Z., Hu, E.J.: Random forest based lung nodule classification aided by clustering. Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc. 34, 535–542 (2010)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Hua, K.L., Hsu, C.H., Hidayati, S.C., Cheng, W.H., Chen, Y.J.: Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Oncotargets Ther 8, 2015–2022 (2015)
Iii, S.G.A., Mclennan, G., Bidaut, L., Mcnittgray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A.: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915–931 (2011)
Lampert, T.A., Stumpf, A., Gançarski, P.: An empirical study into annotator agreement, ground truth estimation, and algorithm evaluation. IEEE Trans. Image Process. 25(6), 2557–2572 (2016)
Lécun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Vedaldi, A., Lenc, K.: MatConvNet - convolutional neural networks for MATLAB. Eprint Arxiv (2014)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
Manivannan, K., Aggarwal, P., Devabhaktuni, V., Kumar, A., Nims, D., Bhattacharya, P.: Particulate matter characterization by gray level co-occurrence matrix based support vector machines. J. Hazard. Mater. 223–224, 94–103 (2012)
Zipser, D., Andersen, R.A.: A back-propagation programmed network that simulates response properties of a subset of posterior. Nature 331, 679–684 (1988)
Firmino, M., Angelo, G., Morais, H., Dantas, M.R., Valentim, R.: Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed. Eng. Online 15, 1–17 (2016)
Arai, K., Okumura, H., Herdiyeni, Y.: Comparison of 2D and 3D local binary pattern in lung cancer diagnosis. Int. J. Adv. Comput. Sci. Appl. 3(4), 89–95 (2012)
Orozco, H.M., Villegas, O.O.V., Sánchez, V.G.C., Alfaro, M.D.J.N.: Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed. Eng. Online 14, 1–20 (2015)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61471297, in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University under Grants Z2017041, and in part by the Australian Research Council (ARC) Grants. We acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xie, Y., Zhang, J., Liu, S., Cai, W., Xia, Y. (2017). Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-61188-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61187-7
Online ISBN: 978-3-319-61188-4
eBook Packages: Computer ScienceComputer Science (R0)