Abstract
Providing efficient access to the huge amounts of existing medical imaging data is a highly relevant but challenging problem. In this paper, we present an effective method for content-based image retrieval (CBIR) of anomalies in medical imaging data, based on similarity of local 3D texture. During learning, a texture vocabulary is obtained from training data in an unsupervised fashion by extracting the dominant structure of texture descriptors. It is based on a 3D extension of the Local Binary Pattern operator (LBP), and captures texture properties via descriptor histograms of supervoxels, or texture bags. For retrieval, our method computes a texture histogram of a query region marked by a physician, and searches for similar bags via diffusion distance. The retrieval result is a ranked list of cases based on the occurrence of regions with similar local texture structure. Experiments show that the proposed local texture retrieval approach outperforms analogous global similarity measures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
André, B., Vercauteren, T., Perchant, A., Buchner, A.M., Wallace, M.B., Ayache, N.: Endomicroscopic image retrieval and classification using invariant visual features. In: Proceedings of the Sixth IEEE International Conference on Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 346–349. IEEE Press, Piscataway (2009)
Depeursinge, A., Iavindrasana, J., Hidki, A., Cohen, G., Geissbühler, A., Platon, A., Poletti, P., Müller, H.: Comparative performance analysis of state-of-the-art classification algorithms applied to lung tissue categorization. J. Digital Imaging 23(1), 18–30 (2010)
Depeursinge, A., Vargas, A., Gaillard, F., Platon, A., Geissbuhler, A., Poletti, P., Müller, H.: Content-based retrieval and analysis of HRCT images from patients with interstitial lung diseases: a comprehesive diagnostic aid framework. In: Computer Assited Radiology and Surgery (CARS) 2010 (June 2010)
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.–A., Müller, H.: 3D Case–Based Retrieval for Interstitial Lung Diseases. In: Caputo, B., Müller, H., Syeda-Mahmood, T., Duncan, J.S., Wang, F., Kalpathy-Cramer, J. (eds.) MCBR-CDS 2009. LNCS, vol. 5853, pp. 39–48. Springer, Heidelberg (2010)
Depeursinge, A., Zrimec, T., Busayarat, S., Müller, H.: 3D lung image retrieval using localized features. In: Medical Imaging 2011: Computer-Aided Diagnosis, vol. 7963, p. 79632E. SPIE (February 2011)
Fehr, J., Burkhardt, H.: 3D rotational invariant local binary patterns. In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Florida, USA, pp. 1–4 (2008)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 264 (2003)
Fritz, M., Schiele, B.: Towards Unsupervised Discovery of Visual Categories. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 232–241. Springer, Heidelberg (2006)
Grauman, K., Darrell, T.: Unsupervised learning of categories from sets of partially matching image features. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 19–25. IEEE Computer Society (2006)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3, 610–621 (1973)
Hou, Z.: A Review on MR Image Intensity Inhomogeneity Correction. International Journal of Biomedical Imaging, 1–12 (2006)
Leung, T., Malik, J.: Recognizing surfaces using three-dimensional textons. In: Proceedings of the International Conference on Computer Vision, ICCV 1999, vol. 2, pp. 1010–1017. IEEE Computer Society, Washington, DC (1999)
Ling, H., Okada, K.: Diffusion distance for histogram comparison. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 246–253 (2006)
Mäenpää, T.: The Local Binary Pattern Approach To Texture Analysis Extensions And Applications (Academic Dissertation), p 20. University of Oulu (August 2003)
Mäenpää, T., Pietikäinen, M.: Texture Analysis With Local Binary Patterns, pp. 197–216. World Scientific Publishing Co. (January 2005)
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vision 43, 7–27 (2001)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18, 837–842 (1996)
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, vol. 1, pp. 582–585. IEEE (1994)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)
van Rikxoort, E., Galperin-Aizenberg, M., Goldin, J., Kockelkorn, T., van Ginneken, B., Brown, M.: Multi-classifier semi-supervised classification of tuberculosis patterns on chest ct scans. In: The Third International Workshop on Pulmonary Image Analysis, pp. 41–48 (2010)
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477. IEEE Computer Society (2003)
Sørensen, L., Shaker, S.B., de Bruijne, M.: Texture Classification in Lung CT using Local Binary Patterns. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 934–941. Springer, Heidelberg (2008)
Tolouee, A., Abrishami-Moghaddam, H., Garnavi, R., Forouzanfar, M., Giti, M.: Texture analysis in lung HRCT images. Digital Image Computing: Techniques and Applications, 305–311 (2008)
Wildenauer, H., Mičušík, B., Vincze, M.: Efficient Texture Representation using Multi-Scale Regions. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 65–74. Springer, Heidelberg (2007)
Zavaletta, V.A., Bartholmai, B.J., Robb, R.A.: High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis. Academic Radiology 14(7), 772–787 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Burner, A., Donner, R., Mayerhoefer, M., Holzer, M., Kainberger, F., Langs, G. (2012). Texture Bags: Anomaly Retrieval in Medical Images Based on Local 3D-Texture Similarity. In: Müller, H., Greenspan, H., Syeda-Mahmood, T. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2011. Lecture Notes in Computer Science, vol 7075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28460-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-28460-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28459-5
Online ISBN: 978-3-642-28460-1
eBook Packages: Computer ScienceComputer Science (R0)