Abstract
Collimation is widely used for X-ray examinations to reduce the overall radiation exposure to the patient and improve the contrast resolution in the region of interest (ROI), that has been exposed directly to X-rays. It is desirable to detect the region of interest and exclude the unexposed area to optimize the image display. Although we only focus on the X-ray images generated with a rectangular collimator, it remains a challenging task because of the large variability of collimated images. In this study, we detect the region of interest as an optimal quadrilateral, which is the intersection of the optimal group of four half-planes. Each half-plane is defined as the positive side of a directed straight line. We develop an extended Hough transform for directed straight lines on a model-aware gray level edge-map, which is estimated with random forests [1] on features of pairs of superpixels. Experiments show that our algorithm can extract the region of interest quickly and accurately, despite variations in size, shape and orientation, and incompleteness of boundaries.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Luo, J., Senn, R.A.: Collimation detection for digital radiography. In: SPIE (1997)
Sheth, R., et al.: Region of interest identification in collimated x-ray images utilizing nonlinear preprocessing and the radon transform. J. Electron. Imaging 14(3), 033011 (2005)
Kawashita, I., et al.: Collimation detection in digital radiographs using plane detection hough transform. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2774. Springer, Heidelberg (2003)
Mao, H., et al.: Multi-view learning based robust collimation detection in digital radiographs. In: SPIE Medical Imaging (2014)
Achanta, R., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11), 2274–2282 (2012)
Andrews, S., Hamarneh, G.: The generalized log-ratio transformation: learning shape and adjacency priors for simultaneous thigh muscle segmentation. IEEE TMI 34(9), 1773–1787 (2015)
Canny, F.J.: A computational approach to edge detection. IEEE PAMI 8(6), 679–698 (1986)
Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhao, L., Peng, Z., Finkler, K., Jerebko, A., Corso, J.J., Zhou, X.(. (2015). Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-Based Edge Detection. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2015. Lecture Notes in Computer Science(), vol 9467. Springer, Cham. https://doi.org/10.1007/978-3-319-28194-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-28194-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28193-3
Online ISBN: 978-3-319-28194-0
eBook Packages: Computer ScienceComputer Science (R0)