Abstract
Accurate diagnosis of Crohn’s disease (CD) has emerged as an important medical challenge. Because current Magnetic resonance imaging (MRI) analysis approaches rely on extensive manual segmentation for an accurate analysis, we propose a method for the automatic identification and localization of regions in abdominal MR volumes that have been affected by CD. Our proposed approach will serve to augment results from colonoscopy, the current reference standard for CD diagnosis. Intensity statistics, texture anisotropy and shape asymmetry of the 3D regions are used as features to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy based asymmetry calculation method. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.
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References
Avni, U., Greenspan, H., Goldberger, J.: X-ray Categorization and Spatial Localization of Chest Pathologies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 199–206. Springer, Heidelberg (2011)
Bauer, S., Nolte, L.-P., Reyes, M.: Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)
Berks, M., Chen, Z., Astley, S., Taylor, C.: Detecting and Classifying Linear Structures in Mammograms Using Random Forests. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 510–524. Springer, Heidelberg (2011)
Bhushan, M., Schnabel, J.A., Risser, L., Heinrich, M.P., Brady, J.M., Jenkinson, M.: Motion Correction and Parameter Estimation in dceMRI Sequences: Application to Colorectal Cancer. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 476–483. Springer, Heidelberg (2011)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Cheng, J., Tao, D., Liu, J., Wong, D.W.K., Lee, B.H., Baskaran, M., Wong, T.Y., Aung, T.: Focal Biologically Inspired Feature for Glaucoma Type Classification. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 91–98. Springer, Heidelberg (2011)
Fuchs, T.J., Buhmann, J.M.: Computational pathology: Challenges and promises for tissue analysis. Comp. Med. Imag. Graphics 35(7-8), 515–530 (2011)
Julesz, B., Gilbert, E.N., Shepp, L.A., Frisch, H.L.: Inability of humans to discriminate between visual textures that agree in second-order statistics – revisited. Perception 2(4), 391–405 (1973)
Kelm, B.M., Mittal, S., Zheng, Y., Tsymbal, A., Bernhardt, D., Vega-Higuera, F., Zhou, S.K., Meer, P., Comaniciu, D.: Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 25–32. Springer, Heidelberg (2011)
Kovalev, V.A., Petrou, M., Bondar, Y.S.: Texture anisotropy in 3D images. IEEE Trans. Imag. Proc. 8(3), 346–360 (1999)
Liu, Z., Smith, L., Sun, J., Smith, M., Warr, R.: Biological Indexes Based Reflectional Asymmetry for Classifying Cutaneous Lesions. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 124–132. Springer, Heidelberg (2011)
Mary, J.Y., Modigliani, R.: Development and validation of an endoscopic index of the severity for crohn’s disease: a prospective multicentre study. Gut. 30(7), 983–989 (1989)
Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled decision forests and their application for semantic segmentation of ct images. In: MICCAI, pp. 184–196 (2011)
Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Möller, A.M., Nekolla, S., Navab, N.: Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 239–247. Springer, Heidelberg (2011)
Petrou, M., Kovalev, V.A., Reichenbach, J.R.: Three-dimensional nonlinear invisible boundary detection. IEEE Trans. Imag. Proc. 15(10), 3020–3032 (2006)
Rimola, J., Rodriguez, S., garcia Bosch, O., et al.: Magnetic resonance for assessment of disease activity and severity in ileocolonic Crohn’s disease. Gut. 58, 1113–1120 (2009)
Schunk, K.: Small bowel magnetic resonance imaging for inflammatory bowel disease (2002)
Verma, R., Zacharaki, E., Ou, Y., Cai, H., Chawla, S., Lee, S., Melhem, E., Wolf, R., Davatzikos, C.: Multiparametric tissue characterization of Brain Neoplasms and their recurrence usng pattern classification of MR images. Acad. Radiol. 15(8), 966–977 (2008)
Zhan, Y., Shen, D.: Deformable segmentation of 3-d ultrasound prostate images using statistical texture matching method. IEEE Trans. Med. Imag. 25(3), 256–272 (2006)
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3), 856–867 (2011)
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Mahapatra, D., Schueffler, P., Tielbeek, J.A.W., Buhmann, J.M., Vos, F.M. (2012). A Supervised Learning Based Approach to Detect Crohn’s Disease in Abdominal MR Volumes. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2012. Lecture Notes in Computer Science, vol 7601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33612-6_11
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DOI: https://doi.org/10.1007/978-3-642-33612-6_11
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