Abstract
Anatomical point landmarks as most primitive anatomical knowledge are useful for medical image understanding. In this study, we propose a detection method for anatomical point landmark based on appearance models, which include gray-level statistical variations at point landmarks and their surrounding area. The models are built based on results of Principal Component Analysis (PCA) of sample data sets. In addition, we employed generative learning method by transforming ROI of sample data. In this study, we evaluated our method with 24 data sets of body trunk CT images and obtained 95.8 ± 7.3 % of the average sensitivity in 28 landmarks.
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
Rohr, K.: Landmark-based image analysis, pp. 26–28. Kluwer Academic Publishers, Dordrecht (2001)
Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis 1(4), 543–563 (2009)
Masutani, Y., Hontani, H., Imiya, A.: Mathematical Foundations of Computational Anatomy. In: Proc. of 1st Int. Symp. on the Project “Computational Anatomy”, pp. 45–51 (2010)
Tomasi, C., Kanade, T.: Shape and Motion from Image Streams under Orthography: a Factorization Method. Int. J. Computer Vosion 9, 137–154 (1992)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual Categorization with Bags of Keypoints. In: Proc. Workshop on Statistical Learning in Computer Vision (2004)
Rohr, K., Stiehl, H.S., Sprengal, R., et al.: Landmark-based elastic registration using approximating thin-plate spline. IEEE Trans. Med. Imag. 20, 526–534 (2001)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. IEEE Int. Conf. on Computer Vision, pp. 1150–1157 (1999)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Proc. European Conf. on Computer Vision, pp. 404–415 (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. IEEE Conf. on CVPR, vol. 1, pp. 886–893 (2005)
Cheung, W., Hamarneh, G.: NSIFT: N-dimensional scale invariant feature transform for matching medical images. In: IEEE Int. Symp. on Biomedical Imaging, pp. 720–723 (2007)
Allaire, S., Kim, J.J., et al.: Full orientation invariance and improved feature selectivity on 3D SIFT with application to medical image analysis. In: IEEE Comp. MMBIA (2008)
Hanaoka, S., Masutani, Y., et al.: Distance-based anatomical landmark detection based on combination optimization in candidate groups by using Gibbs’ sampling (in preparation for SPIE 2011)
Ishida, H., Yanadume, S., Takahashi, T., Ide, I., Mekada, Y., Murase, H.: Recognition of Low-Resolution Characters by a Generative Learning Method. In: Proc. CBDAR, pp. 45–51 (2005)
Lorenz, C., Carlsen, I.C., Buzug, T.M., et al.: Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 233–242. Springer, Heidelberg (1997)
Nemoto, M., Masutani, Y., et al.: Appearance-based detection and MadaBoost-based Classification of anatomical landmark candidates (in preparation for SPIE 2011)
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
Nemoto, M. et al. (2010). Preliminary Study on Appearance-Based Detection of Anatomical Point Landmarks in Body Trunk CT Images. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-15948-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15947-3
Online ISBN: 978-3-642-15948-0
eBook Packages: Computer ScienceComputer Science (R0)