Abstract
In this paper, we propose to classify medical images using dissimilarities computed between collections of regions of interest. The images are mapped into a dissimilarity space using an image dissimilarity measure, and a standard vector space-based classifier is applied in this space. The classification output of this approach can be used in computer aided-diagnosis problems where the goal is to detect the presence of abnormal regions or to quantify the extent or severity of abnormalities in these regions. The proposed approach is applied to quantify chronic obstructive pulmonary disease in computed tomography (CT) images, achieving an area under the receiver operating characteristic curve of 0.817. This is significantly better compared to combining individual region classifications into an overall image classification, and compared to common computerized quantitative measures in pulmonary CT.
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Keywords
- Chronic Obstructive Pulmonary Disease
- Compute Tomography Image
- Multiple Instance Learning
- Pulmonary Compute Tomography
- Response Histogram
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Müller, N.L., Staples, C.A., Miller, R.R., Abboud, R.T.: “Density mask”. An objective method to quantitate emphysema using computed tomography. Chest 94(4), 782–787 (1988)
van Ginneken, B., Katsuragawa, S., ter Haar Romeny, B., Doi, K., Viergever, M.: Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Trans. Med. Imag. 21(2), 139–149 (2002)
Park, Y.S., Seo, J.B., Kim, N., Chae, E.J., Oh, Y.M., Lee, S.D., Lee, Y., Kang, S.H.: Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: comparison with density-based quantification and correlation with pulmonary function test. Invest Radiol. 43(6), 395–402 (2008)
Raundahl, J., Loog, M., Pettersen, P., Tanko, L.B., Nielsen, M.: Automated effect-specific mammographic pattern measures. IEEE Trans. Med. Imag. 27(8), 1054–1060 (2008)
Arzhaeva, Y., Hogeweg, L., de Jong, P.A., Viergever, M.A., van Ginneken, B.: Global and local multi-valued dissimilarity-based classification: Application to computer-aided detection of tuberculosis. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 724–731. Springer, Heidelberg (2009)
Sørensen, L., Lo, P., Ashraf, H., Sporring, J., Nielsen, M., de Bruijne, M.: Learning COPD sensitive filters in pulmonary CT. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 699–706. Springer, Heidelberg (2009)
Sørensen, L., Shaker, S.B., de Bruijne, M.: Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans. Med. Imag. 29(2), 559–569 (2010)
Pekalska, E., Duin, R.P.W.: Dissimilarity representations allow for building good classifiers. Pattern Recog. Lett. 23(8), 943–956 (2002)
Eiter, T., Mannila, H.: Distance measures for point sets and their computation. Acta Inf. 34(2), 109–133 (1997)
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)
Rabe, K.F., Hurd, S., Anzueto, A., Barnes, P.J., Buist, S.A., Calverley, P., Fukuchi, Y., Jenkins, C., Rodriguez-Roisin, R., van Weel, C., Zielinski, J.: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am. J. Respir. Crit. Care Med. 176(6), 532–555 (2007)
Kittler, J., Alkoot, F.M.: Moderating k-NN classifiers. Pattern Anal. Appl. 5(3), 326–332 (2002)
Loog, M., Van Ginneken, B.: Static posterior probability fusion for signal detection: applications in the detection of interstitial diseases in chest radiographs. In: ICPR (1), pp. 644–647. IEEE Computer Society, Los Alamitos (2004)
Webb, W.R., Müller, N., Naidich, D.: High-Resolution CT of the Lung, 3rd edn. Lippincott Williams & Wilkins (2001)
DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3), 837–845 (1988)
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1-2), 31–71 (1997)
Gärtner, T., Flach, P.A., Kowalczyk, A., Smola, A.J.: Multi-instance kernels. In: ICML, pp. 179–186. Morgan Kaufmann, San Francisco (2002)
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Sørensen, L. et al. (2010). Image Dissimilarity-Based Quantification of Lung Disease from CT. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_5
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