Abstract
This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of \(89\,\%\) if both exact and one-off predictions are considered correct.
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References
Regan, E.A., Hokanson, J.E., Murphy, J.R., Make, B., Lynch, D.A., Beaty, T.H., Curran-Everett, D., Silverman, E.K., Crapo, J.D.: Genetic epidemiology of COPD (COPDGene) study design. COPD: J. Chronic Obstr. Pulm. Dis. 7(1), 32–43 (2011)
Gerber, S., Tasdizen, T., Thomas Fletcher, P., Joshi, S., Whitaker, R.: Manifold modeling for brain population analysis. Med. Image Anal. 14(5), 643–653 (2010)
Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: Gram: a framework for geodesic registration on anatomical manifolds. Med. Image Anal. 14(5), 633–642 (2010)
Toews, M., Zöllei, L., Wells, W.M.: Feature-based alignment of volumetric multi-modal images. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 25–36. Springer, Heidelberg (2013)
Wachinger, C., Golland, P., Reuter, M.: BrainPrint: identifying subjects by their brain. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part III. LNCS, vol. 8675, pp. 41–48. Springer, Heidelberg (2014)
Brin, S.: Near neighbor search in large metric spaces. In: VLDB, pp. 574–584 (1995)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Toews, M., Wells III, W.M.: Efficient and robust model-to-image alignment using 3D scale-invariant features. Med. Image Anal. 17(3), 271–282 (2013)
Muja, M., Lowe, D.: Scalable nearest neighbour algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014)
Kleinberg, J.M.: Two algorithms for nearest-neighbor search in high dimensions. In: ACM symposium on Theory of computing, pp. 599–608 (1997)
Terrell, G.R., Scott, D.W.: Variable kernel density estimation. Ann. Stat. 20, 1236–1265 (1992)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Sorensen, L., Nielsen, M., Lo, P., Ashraf, H., Pedersen, J.H., De Bruijne, M.: Texture-based analysis of COPD: a data-driven approach. IEEE Trans. Med. Imaging 31(1), 70–78 (2012)
Moravec, H.P.: Visual mapping by a robot rover. In: Proceedings of the 6th International Joint Conference on Artificial Intelligence, pp. 598–600 (1979)
Lindeberg, T.: Feature detection with automatic scale selection. IJCV 30(2), 79–116 (1998)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. IJCV 60(1), 63–86 (2004)
Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nat. Neurosci. 5(7), 682–687 (2002)
Toussaint, G.T.: Proximity graphs for nearest neighbor decision rules: recent progress. In: Interface 34 (2002)
Yu, A., Grauman, K.: Predicting useful neighborhoods for lazy local learning. In: Advances in Neural Information Processing Systems, pp. 1916–1924 (2014)
Breiman, L., Meisel, W., Purcell, E.: Variable kernel estimates of multivariate densities. Technometrics 19(2), 135–144 (1977)
Toews, M., Wells III, W.M., Collins, D.L., Arbel, T.: Feature-based morphometry: discovering group-related anatomical patterns. NeuroImage 49(3), 2318–2327 (2010)
Gill, G., Toews, M., Beichel, R.R.: Robust initialization of active shape models for lung segmentation in CT scans: a feature-based atlas approach. Int. J. Biomed. Imaging 2014, Article ID 479154, 7p (2014)
Singh, N., Thomas Fletcher, P., Samuel Preston, J., King, R.D., Marron, J., Weiner, M.W., Joshi, S.: Quantifying anatomical shape variations in neurological disorders. Med. Image Anal. 18(3), 616–633 (2014)
Aljabar, P., Wolz, R., Srinivasan, L., Counsell, S.J., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: A combined manifold learning analysis of shape and appearance to characterize neonatal brain development. IEEE Trans. Med. Imaging 30(12), 2072–2086 (2011)
Ye, D.H., Hamm, J., Kwon, D., Davatzikos, C., Pohl, K.M.: Regional manifold learning for deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 131–138. Springer, Heidelberg (2012)
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46(3), 726–738 (2009)
Song, Y., Cai, W., Zhou, Y., Feng, D.D.: Feature-based image patch approximation for lung tissue classification. IEEE TMI 32(4), 797–808 (2013)
Mets, O.M., Buckens, C.F., Zanen, P., Isgum, I., van Ginneken, B., Prokop, M., Gietema, H.A., Lammers, J.W.J., Vliegenthart, R., Oudkerk, M., et al.: Identification of chronic obstructive pulmonary disease in lung cancer screening computed tomographic scans. JAMA 306(16), 1775–1781 (2011)
Gu, S., Leader, J., Zheng, B., Chen, Q., Sciurba, F., Kminski, N., Gur, D., Pu, J.: Direct assessment of lung function in COPD using CT densitometric measures. Physiol. Meas. 35(5), 833 (2014)
Toews, M., Wells III, W.M.: SIFT-RANK: ordinal description for invariant feature correspondence. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 172–177. IEEE (2009)
Guld, M.O., Kohnen, M., Keysers, D., Schubert, H., Wein, B., Bredno, J., Lehmann, T.M.: Quality of DICOM header information for image categorization. In: International Symposium on Medical Imaging, vol. 4685, pp. 280–287. SPIE (2002)
Torki, M., Elgammal, A.: Putting local features on a manifold. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1743–1750. IEEE (2010)
Acknowledgements
This research was supported by NIH grants P41EB015902, P41EB015898 5K25HL104085, 5R01HL116931 and 5R01HL116473.
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Toews, M., Wachinger, C., Estepar, R.S.J., Wells, W.M. (2015). A Feature-Based Approach to Big Data Analysis of Medical Images. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_26
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