Abstract
In this paper we propose a novel learning–based CBIR method for fast content–based retrieval of similar 3D images based on the intrinsic Random Forest (RF) similarity. Furthermore, we allow the combination of flexible user–defined semantics (in the form of retrieval contexts and high–level concepts) and appearance–based (low–level) features in order to yield search results that are both meaningful to the user and relevant in the given clinical case. Due to the complexity and clinical relevance of the domain, we have chosen to apply the framework to the retrieval of similar 3D CT hepatic pathologies, where search results based solely on similarity of low–level features would be rarely clinically meaningful. The impact of high–level concepts on the quality and relevance of the retrieval results has been measured and is discussed for three different set–ups. A comparison study with the commonly used canonical Euclidean distance is presented and discussed as well.
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.
References
Lencioni, R., Cioni, D., Bartolozzi, C., Baert, A.L.: Focal Liver Lesions: Detection, Characterization, Ablation. Springer, Heidelberg (2005)
Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content–based image retrieval: current status and future directions. Journal of Digital Imaging (2010)
Napel, S.A., Beaulieu, C.F., Rodriguez, C., Cui, J., Xu, J., Gupta, A., Korenblum, D., Greenspan, H., Ma, Y., Rubin, D.L.: Automated retrieval of CT images of liver lesions on the basis of image similarity: Method and preliminary results. Radiology 256(1) (2010)
Spencer, J.A.: Indeterminate lesions in cancer imaging. Clinical Radiology 63, 843–852 (2008)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content–based image retrieval at the end of the early years. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(12) (2000)
Segal, E., Sirlin, C.B., Ooi, C., Adler, A.S., Gollub, J., Chen, X., Chan, B.K., Matchuk, G.R., Barry, C.T., Chang, H.Y., Kuo, M.D.: Decoding global gene expression programs in liver cancer by noninvasive imaging. Nature Biotechnology (2007)
Seifert, S., Thoma, M., Stegmaier, F., Hammon, M., Kramer, M., Huber, M., Kriegel, H.-P., Cavallaro, A., Comaniciu, D.: Combined semantic and similarity search in medical image databases 7967, 7967-2 (2011)
Pękalska, E.z., Harol, A., Duin, R.P.W., Spillmann, B., Bunke, H.: Non-Euclidean or Non-Metric Measures Can be Informative. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 871–880. Springer, Heidelberg (2006)
Tsymbal, A., Huber, M., Zhou, S.K.: Learning discriminative distance functions for case retrieval and decision support. Transactions on CBR 3(1), 1–16 (2010)
Shi, T., Seligson, D., Belldegrun, A.S., Palotie, A., Horvath, S.: Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma. Mod Pathol. 18(4), 547–557 (2005)
Shi, T., Horvath, S.: Unsupervised learning with random forest predictors. Computational and Graphical Statistics 15(1), 118–138 (2006)
Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., Falkowski, M.J.: Nearest neighbour imputation of species-level, plot-scale forest structure attributes from lidar data. Remote Sensing of Environment 112(5), 2232–2245 (2008)
Qi, Y., Klein-Seetharaman, J., Bar-Joseph, Z.: Random forest similarity for protein–protein interaction prediction from multiple sources. In: Prooceedings of Pacific Symposium on Biocomputing (2005)
Vitanovski, D., Tsymbal, A., Ionasec, R., Georgescu, B., Zhou, S.K., Comaniciu, D.: Learning distance function for regression-based 4d pulmonary trunk model reconstruction estimated from sparse MRI data. In: Proc. SPIE Medical Imaging (2011)
Breiman, L.: Random forests. Machine Learning, 5–32 (2001)
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: Online random forests. In: 3rd IEEE ICCV Workshop on Online Computer Vision (2009)
Oza, N., Russell, S.: Experimental comparisons of online and batch versions of bagging and boosting, pp. 359–364 (2001)
Pfahringer, B., Holmes, G., Kirkby, R.: New options for Hoeffding trees. In: Australian Conference on AI (2007)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inform. Theory 8 (1962)
Pejnovic, P., Buturovic, L., Stojiljkovic, Z.: Object recognition by invariants. In: Proceedings of Int. Conf. on Pattern Recognition (1992)
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
Costa, M.J. et al. (2012). A Discriminative Distance Learning–Based CBIR Framework for Characterization of Indeterminate Liver Lesions. 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_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-28460-1_9
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)