Abstract
Interstitial lung disease (ILD) is a multifactorial condition that is difficult to diagnose. High-resolution computed tomography (CT) is commonly the imaging modality of choice, as it enables detection and mapping of distinctive pathological patterns. The distribution of these patterns gives clues as to the correct histological diagnosis. This paper compares two approaches to detecting these complex patterns: “man-made” features, based on classical handcrafted texture descriptors, and “machine-made” features, built with deep learning convolutional neural networks (CNNs). The two paradigms are evaluated on scans from 132 subjects, derived from two public databases of high resolution ILD CT images and associated expert annotations. Five specific tissue patterns are included: healthy, emphysema, fibrosis, ground glass opacity, and micronodules. The subjects are divided into training, validation and test groups. On the validation data the best handcrafted solution achieves a class assignment accuracy of 76.0%, compared with the best deep learning accuracy of 79.0%. For the test group, which was not used during development and only tested once, the handcrafted method achieves 65.5%, compared with the CNN accuracy of 69.9%. The results indicate that deep learning CNNs can outperform traditional texture measures, even on a low-level texture classification task such as this.
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References
Amadasun, M., King, R.: Texural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19(5), 1264–1274 (1989)
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.A., Müller, H.: Building a reference multimedia database for interstitial lung diseases. Comput. Med. Imaging Graph. 36(3), 227–238 (2012)
Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graph. Image Process. 4(2), 172–179 (1975)
Gao, M., Xu, Z., Lu, L., Harrison, A.P., Summers, R.M., Mollura, D.J.: Multi-label deep regression and unordered pooling for holistic interstitial lung disease pattern detection. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 147–155. Springer, Cham (2016). doi:10.1007/978-3-319-47157-0_18
Grenier, P., Valeyre, D., Cluzel, P., Brauner, M.W., Lenoir, S., Chastang, C.: Chronic diffuse interstitial lung disease: diagnostic value of chest radiography and high-resolution CT. Radiology 179(1), 123–132 (1991)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. CoRR, abs/1502.0 (2015)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, pp. 1–15 (2015)
Kononenko, I., Šimec, E., Robnik-Šikonja, M.: Overcoming the myopia of inductive learning algorithms with RELIEFF. Appl. Intell. 7(1), 39–55 (1997)
Laws, K.I.: Textured image segmentation. Technical report, University of Southern California, January 1980
Li, Q., Cai, W., Feng, D.D.: Lung image patch classification with automatic feature learning. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6079–6082 (2013)
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 13th International Conference on Control, Automation, Robotics, and Vision, pp. 844–848, December 2014
Lopes, R., Betrouni, N.: Fractal and multifractal analysis: a review. Med. Image Anal. 13(4), 634–649 (2009)
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Sørensen, L., Shaker, S.B., De Bruijne, M.: Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans. Med. Imaging 29(2), 559–569 (2010)
Uppaluri, R., Heitmman, E.A., Sonka, M., Hartley, P.G., Hunninghake, G.W., Mclennan, G.: Computer recognition of regional lung disease patterns. Am. J. Respir. Crit. Care Med. 160(2), 648–654 (1999)
van Tulder, G., de Bruijne, M.: Learning features for tissue classification with the classification restricted Boltzmann machine. In: Menze, B., et al. (eds.) MCV 2014. LNCS, vol. 8848, pp. 47–58. Springer, Cham (2014). doi:10.1007/978-3-319-13972-2_5
Van Tulder, G., De Bruijne, M.: Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines. IEEE Trans. Med. Imaging 35(5), 1262–1272 (2016)
Wang, J.S., Cherng, J.M., Perng, D.S., Lee, H.S., Wang, S.: High resolution computed tomography in assessment of patients with emphysema. Respir. Care 58(4), 614–622 (2013)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Adv. Neural Inf. Proc. Syst. 27 (Proc. NIPS) 27, 1–9 (2014)
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O’Neil, A., Shepherd, M., Beveridge, E., Goatman, K. (2017). A Comparison of Texture Features Versus Deep Learning for Image Classification in Interstitial Lung Disease. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_65
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