Abstract
In this paper, a computer–aided diagnosis (CAD) system that retrieves similar cases affected with an interstitial lung disease (ILDs) to assist the radiologist in the diagnosis workup is presented and evaluated. The multimodal inter–case distance measure is based on a set of clinical parameters as well as automatically segmented 3–dimensional regions of lung tissue in high–resolution computed tomography (HRCT) of the chest. A global accuracy of 75.1% of correct matching among five classes of lung tissues as well as a mean average retrieval precision at rank 1 of 71% show that automated lung tissue categorization in HRCT data is complementary to case–based retrieval both from the user’s viewpoint and also on the algorithmic side.
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
Aziz, Z.A., Wells, A.U., Hansell, D.M., Bain, G.A., Copley, S.J., Desai, S.R., Ellis, S.M., Gleeson, F.V., Grubnic, S., Nicholson, A.G., Padley, S.P., Pointon, K.S., Reynolds, J.H., Robertson, R.J., Rubens, M.B.: HRCT diagnosis of diffuse parenchymal lung disease: inter–observer variation. Thorax 59(6), 506–511 (2004)
Sluimer, I.C., Schilham, A., Prokop, M., van Ginneken, B.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Transactions on Medical Imaging 25(4), 385–405 (2006)
Shyu, C.R., Brodley, C.E., Kak, A.C., Kosaka, A., Aisen, A.M., Broderick, L.S.: ASSERT: A physician–in–the–loop content–based retrieval system for HRCT image databases. Computer Vision and Image Understanding (special issue on content–based access for image and video libraries) 75(1/2), 111–132 (1999)
Uppaluri, R., Hoffman, E.A., Sonka, M., Hunninghake, G.W., McLennan, G.: Interstitial lung disease: A quantitative study using the adaptive multiple feature method. American Journal of Respiratory and Critical Care Medicine 159(2), 519–525 (1999)
Fetita, C.I., Chang-Chien, K.-C., Brillet, P.-Y., Prêteux, F., Grenier, P.: Diffuse parenchymal lung diseases: 3D automated detection in MDCT. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 825–833. Springer, Heidelberg (2007)
Webb, W.R., Müller, N.L., Naidich, D.P. (eds.): High–Resolution CT of the Lung. Lippincott Williams & Wilkins, Philadelphia (2001)
Kim, N., Seo, J.B., Lee, Y., Lee, J.G., Kim, S.S., Kang, S.H.: Development of an automatic classification system for differentiation of obstructive lung disease using HRCT. Journal of Digital Imaging 22(2), 136–148 (2009)
Dundar, M., Fung, G., Bogoni, L., Macari, M., Megibow, A., Rao, B.: A methodology for training and validating a cad system and potential pitfalls. In: CARS 2004 – Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and Exhibition. International Congress Series, vol. 1268, pp. 1010–1014 (2004)
Müller, H., Clough, P., Hersh, B., Geissbühler, A.: Variation of relevance assessments for medical image retrieval. In: Marchand-Maillet, S., Bruno, E., Nürnberger, A., Detyniecki, M. (eds.) AMR 2006. LNCS, vol. 4398, pp. 232–246. Springer, Heidelberg (2007)
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content–based image retrieval systems in medicine – clinical benefits and future directions. International Journal of Medical Informatics 73(1), 1–23 (2004)
Müller, H., Rosset, A., Garcia, A., Vallée, J.P., Geissbuhler, A.: Benefits from content–based visual data access in radiology. RadioGraphics 25(3), 849–858 (2005)
Caritá, E.C., Seraphim, E., Honda, M.O., Mazzoncini de Azevedo-Marques, P.: Implementation and evaluation of a medical image management system with content– based retrieval support. Radiologia Brasileira 41(5), 331–336 (2008)
Aisen, A.M., Broderick, L.S., Winer-Muram, H., Brodley, C.E., Kak, A.C., Pavlopoulou, C., Dy, J., Shyu, C.R., Marchiori, A.: Automated storage and retrieval of thin–section CT images to assist diagnosis: System description and preliminary assessment. Radiology 228(1), 265–270 (2003)
Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing 4(11), 1549–1560 (1995)
Van De Ville, D., Blu, T., Unser, M.: Isotropic polyharmonic B–Splines: Scaling functions and wavelets. IEEE Transactions on Image Processing 14(11), 1798–1813 (2005)
Depeursinge, A., Van De Ville, D., Unser, M., Müller, H.: Lung tissue analysis using isotropic polyharmonic B–spline wavelets. In: MICCAI 2008 Workshop on Pulmonary Image Analysis, New York, USA, Lulu, September 2008, pp. 125–134 (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 Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Depeursinge, A., Racoceanu, D., Iavindrasana, J., Cohen, G., Platon, A., Poletti, P.A., Müller, H.: Fusing visual and clinical information for lung tissue classification in HRCT data. Journal of Artificial Intelligence in Medicine (to appear, 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P., Müller, H. (2010). 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) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2009. Lecture Notes in Computer Science, vol 5853. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11769-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-11769-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11768-8
Online ISBN: 978-3-642-11769-5
eBook Packages: Computer ScienceComputer Science (R0)