Abstract
Although radiologists can employ CAD systems to characterize malignancies, pulmonary fibrosis and other chronic diseases; the design of imaging techniques to quantify infectious diseases continue to lag behind. There exists a need to create more CAD systems capable of detecting and quantifying characteristic patterns often seen in respiratory tract infections such as influenza, bacterial pneumonia, or tuborculosis. One of such patterns is Tree-in-bud (TIB) which presents thickened bronchial structures surrounding by clusters of micro-nodules. Automatic detection of TIB patterns is a challenging task because of their weak boundary, noisy appearance, and small lesion size. In this paper, we present two novel methods for automatically detecting TIB patterns: (1) a fast localization of candidate patterns using information from local scale of the images, and (2) a Möbius invariant feature extraction method based on learned local shape and texture properties. A comparative evaluation of the proposed methods is presented with a dataset of 39 laboratory confirmed viral bronchiolitis human parainfluenza (HPIV) CTs and 21 normal lung CTs. Experimental results demonstrate that the proposed CAD system can achieve high detection rate with an overall accuracy of 90.96%.
Chapter PDF
Similar content being viewed by others
References
Eisenhuber, E.: The tree-in-bud sign. Radiology 222(3), 771–772 (2002)
Saha, P.K., Udupa, J.K., Odhner, D.: Scale-based fuzzy connected image segmentation: Theory, algorithms, and validation. Computer Vision Image Understanding 77, 145–174 (2000)
Hu, S., Hoffman, E.A., Reinhardt, J.M.: Automatic lung segmentation for accurate quantification of volumetric X-ray CT images. Transactions on Medical Imaging 20(6), 490–498 (2001)
Bobenko, E.I., Shroder, P.: Discrete Willmore Flow. In: Eurographics Symposium on Geometry Processing, pp. 101–110 (2005)
Lillholm, M., Griffin, L.D.: Statistics and category systems for the shape index descriptor of local 2nd order natural image structure. Image and Vision Computing 27(6), 771–781 (2009)
Ayed, I.B., Mitiche, A., Belhadj, Z.: Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets. Transactions on Pattern Analysis and Machine Intelligence 28(9), 1493–1500 (2006)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 610–621 (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bagci, U., Yao, J., Caban, J., Suffredini, A.F., Palmore, T.N., Mollura, D.J. (2011). Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-23626-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23625-9
Online ISBN: 978-3-642-23626-6
eBook Packages: Computer ScienceComputer Science (R0)