Abstract
Bone image segmentation is an integral component of orthopedic X-ray image analysis that aims at extracting the bone structure from the muscles and tissues. Automatic segmentation of the bone part in a digital X-ray image is a challenging problem because of its low contrast with the surrounding flesh, which itself needs to be discriminated against the background. The presence of noise and spurious edges further complicates the segmentation. In this paper, we propose an efficient entropy-based segmentation technique that integrates several simple steps, which are fully automated. Experiments on several X-ray images reveal encouraging results as evident from a segmentation entropy quantitative assessment (SEQA) metric [Hao, et al. 2009].
Chapter PDF
Similar content being viewed by others
References
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evolution. Journal of Electronic Imaging 13(1), 146–165 (2004)
Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337 (1998)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)
Yang, J., Staib, L.H., Duncan, V.: Neighborhood-constrained segmentation with level based 3-D deformable models. IEEE Transactions on Medical Imaging 23(8), 940–948 (2004)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson, London (2008)
Šćepanović, D., Kirshtein, J., Jain, A.K., Taylor, R.H.: Fast algorithm for probabilistic bone edge detection (FAPBED). In: SPIE, vol. 5747, pp. 1753–1765 (2005)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
Sen, D., Pal, S.K.: Gradient histogram thresholding in a region of interest for edge detection. Image and Vision Computing 28, 677–695 (2010)
Kundu, M.K., Pal, S.K.: Thresholding for edge detection using human psycho-visual phenomena. Pattern Recognition Letters 4(6), 433–441 (1986)
Pal, S.K., King, R.A.: On edge detection of X-ray images using fuzzy set. IEEE Transactions on Pattern Analysis and Machine Intelligence 5, 69–77 (1983)
Schulze, M.A., Pearce, J.A.: Linear combinations of morphological operators: The midrange, pseudomedian, and LOCO filters. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 57–60 (1993)
Ning, J.L., Zhang, J., Zhang, D., Wu, C.: Interactive image segmentation by maximal similarity based region merging. Pattern Recognition 43, 445–456 (2010)
Yan, C., Sang, N., Zhang, T.: Local entropy-based transition region extraction and thresholding. Pattern Recognition Letters 24, 2935–2941 (2003)
Kang, W., Wang, K., Wang, Q., An, D.: Segmentation method based on transition region extraction for coronary angiograms. In: IEEE International Conference on Mechatronics and Automation, pp. 905–909 (2009)
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 23(9), 1277–1294 (1993)
Hao, J., Shen, Y., Xu, H., Zou, J.: A region entropy based objective evaluation method for image segmentation. In: IEEE International Conference on Instrumentation and Measurement Technology, pp. 373–377 (2009)
Zhang, Y.: A survey on evaluation methods for image segmentation. Pattern Recognition 29(8), 1335–1346 (1996)
Ding, F.: Segmentation of bone structure in X-ray images. Thesis Proposal, School of Computing, National University of Singapore (2006)
Liang, J., Pan, B.-C., Fan, Y.-H.: Fracture identification of X-ray image. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 67–73 (2010)
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
Bandyopadhyay, O., Chanda, B., Bhattacharya, B.B. (2011). Entropy-Based Automatic Segmentation of Bones in Digital X-ray Images. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2011. Lecture Notes in Computer Science, vol 6744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21786-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-21786-9_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21785-2
Online ISBN: 978-3-642-21786-9
eBook Packages: Computer ScienceComputer Science (R0)