Abstract
Up to now, for most endoscopical computer aided celiac disease diagnosis approaches, image regions showing discriminative features have to be manually extracted by the physicians, prior to their automatized classification. This is obligatory to get idealistic and reliable data which is free from strong image degradations. On the one hand such a human interaction during endoscopy is subjective, expensive and tedious, but on the other hand state-of-the-art fully automatized selection corresponds to decreased classification accuracies compared to experienced human experts. In this work, a fully automatized approach is introduced which exploits the availability of a significant number of subimages within one original endoscopic image. A weighted decision-level and a weighted feature-level fusion method are introduced and investigated with respect to the achieved classification accuracies. The outcomes are compared with simple decision-level and feature-level fusion methods and the manual and the automatized patch selection. Finally, we show that the proposed feature-level fusion method outperforms all other automatized methods and comes close to manual patch selection.
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Notes
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Runtime tests are executed on an Intel i5 architecture with 3.1 MHz. All functions are implemented in MATLAB 2013a.
References
Atasoy, S., Mateus, D., Lallemand, J., Meining, A., Yang, G.-Z., Navab, N.: Endoscopic video manifolds. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 437–445. Springer, Heidelberg (2010)
Bashar, M., Kitasaka, T., Suenaga, Y., Mekada, Y., Mori, K.: Automatic detection of informative frames from wireless capsule endoscopy images. Med. Image Anal. 14(3), 449–470 (2010)
Ciaccio, E.J., Tennyson, C.A., Bhagat, G., Lewis, S.K., Green, P.H.R.: Classification of videocapsule endoscopy image patterns: comparative analysis between patients with celiac disease and normal individuals. BioMed. Eng. Online 9(1), 1–12 (2010)
Ciaccio, E.J., Tennyson, C.A., Lewis, S.K., Krishnareddy, S., Bhagat, G., Green, P.: Distinguishing patients with celiac disease by quantitative analysis of videocapsule endoscopy images. Comput. Methods Programs Biomed. 100(1), 39–48 (2010)
Fasano, A., Berti, I., Gerarduzzi, T., Not, T., Colletti, R.B., Drago, S., Elitsur, Y., Green, P.H.R., Guandalini, S., Hill, I.D., Pietzak, M., Ventura, A., Thorpe, M., Kryszak, D., Fornaroli, F., Wasserman, S.S., Murray, J.A., Horvath, K.: Prevalence of celiac disease in at-risk and not-at-risk groups in the united states: a large multicenter study. Arch. Intern. Med. 163, 286–292 (2003)
Gadermayr, M., Liedlgruber, M., Uhl, A., Vécsei, A.: Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease. Comput. Methods Programs Biomed. 112(3), 694–712 (2013)
Gadermayr, M., Liedlgruber, M., Uhl, A., Vécsei, A.: Shape curvature histogram: a shape feature for celiac disease diagnosis. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds.) MCV 2013. LNCS, vol. 8331, pp. 175–184. Springer, Heidelberg (2014)
Gadermayr, M., Uhl, A., Vécsei, A.: Barrel-type distortion compensated fourier feature extraction. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Li, B., Porikli, F., Zordan, V., Klosowski, J., Coquillart, S., Luo, X., Chen, M., Gotz, D. (eds.) ISVC 2013, Part I. LNCS, vol. 8033, pp. 50–59. Springer, Heidelberg (2013)
Gadermayr, M., Uhl, A., Vécsei, A.: Getting one step closer to fully automatized celiac disease diagnosis. In: IEEE International Conference on Image Processing Theory, Tools and Applications 2014 (IPTA’14), October 2014
Hegenbart, S., Uhl, A., Vécsei, A.: Impact of endoscopic image degradations on LBP based features using one-class SVM for classification of celiac disease. In: Proceedings of the 7th International Symposium on Image and Signal Processing and Analysis (ISPA’11), Dubrovnik, Croatia, pp. 715–720, September 2011
Hegenbart, S., Uhl, A., Vécsei, A.: Impact of histogram subset selection on classification using multiscale LBP. In: Handels, H., Ehrhardt, J., Deserno, T.M., Meinzer, H.-P., Tolxdorff, T. (eds.) BVM’11. Informatik Aktuell, pp. 359–363. Springer, Heidelberg (2011)
Kuncheva, L.I., Bezdek, J.C., Duin, R.P.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn. 34(2), 299–314 (2001)
Liao, S.C., Zhu, X.X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)
Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T., Sa, G.: A no-reference perceptual blur metric. In: IEEE International Conference on Image Processing (ICIP’02), pp. 57–60 (2002)
Oberhuber, G., Granditsch, G., Vogelsang, H.: The histopathology of coeliac disease: time for a standardized report scheme for pathologists. Eur. J. Gastroenterol. Hepatol. 11, 1185–1194 (1999)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)
Prabhakar, S., Jain, A.K.: Decision-level fusion in fingerprint verification. Pattern Recogn. 35(4), 861–874 (2002)
Raudys, Š., Roli, F.: The behavior knowledge space fusion method: analysis of generalization error and strategies for performance improvement. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 55–64. Springer, Heidelberg (2003)
Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recogn. Lett. 24(13), 2115–2125 (2003)
Uhl, A., Wild, P.: Single-sensor multi-instance fingerprint and eigenfinger recognition using (weighted) score combination methods. Int. J. Biometrics 1(4), 442–462 (2009). Special Issue on Multimodal Biometric and Biometric Fusion
Vécsei, A., Amann, G., Hegenbart, S., Liedlgruber, M., Uhl, A.: Automated marsh-like classification of celiac disease in children using an optimized local texture operator. Comput. Biol. Med. 41(6), 313–325 (2011)
Weile, B., Hansen, B.F., Hägerstrand, I., Hansen, J.P.H., Krasilnikoff, P.A.: Interobserver variation in diagnosing coeliac disease, a joint study by danish and swedish pathologists. APMIS 108(5), 380–384 (2000)
Xu, Y., Ji, H., Fermüller, C.: Viewpoint invariant texture description using fractal analysis. Int. J. Comput. Vis. 83(1), 85–100 (2009)
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Gadermayr, M., Uhl, A., Vécsei, A. (2014). Quality Based Information Fusion in Fully Automatized Celiac Disease Diagnosis. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_55
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DOI: https://doi.org/10.1007/978-3-319-11752-2_55
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