All Title Author
Keywords Abstract


Fracture Detection in Traumatic Pelvic CT Images

DOI: 10.1155/2012/327198

Full-Text   Cite this paper   Add to My Lib

Abstract:

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately. 1. Introduction Pelvic fractures are high energy injuries that constitute a major cause of death in trauma patients. According to the Centers for Disease Control and Prevention (CDC), trauma injury kills more people between the ages of 1 and 44 than any other disease or illness. Among different types of trauma with a high impact on the lives of Americans, traumatic pelvic injuries, caused mainly by sports, falls, and motor vehicle accidents, contribute to a large number of mortalities every year [1, 2]. Traumatic pelvic injuries and associated complications, such as severe hemorrhage multiple organ dysfunction syndrome (MODS), result in the mortality rate from 8.6% to 50% [3]. When combined with other injuries in the body, for instance, the abdomen, the chance of mortality is even higher [4]. In general, a pelvic fracture can be associated hemorrhage, neurologic injury, vascular injury, and organ damage, as all of the vital structures run through pelvis. Pain and impaired mobility are normally the results of nerve and internal organ damage associated with the pelvic fracture [5–7]. Patient data, in particular, medical images such as computed tomography (CT) images, contain a significant amount of information, and it is crucial for physicians to make diagnostic decisions as well as treatment planning on the basis of this information and other patients’ data. Currently, a large portion of the data is not optimally and comprehensively utilized, because information held in the data is inaccessible through visual observation or simple traditional computational methods. Information contained in pelvic CT images is a very important resource for the assessment of the

References

[1]  M. A. Schiff, A. F. Tencer, and C. D. Mack, “Risk factors for pelvic fractures in lateral impact motor vehicle crashes,” Accident Analysis and Prevention, vol. 40, no. 1, pp. 387–391, 2008.
[2]  A. Salim, P. G. R. Teixeira, J. DuBose et al., “Predictors of positive angiography in pelvic fractures: a prospective study,” Journal of the American College of Surgeons, vol. 207, no. 5, pp. 656–662, 2008.
[3]  University of Maryland National Study Center for Trauma/EMS, “Lower extremity injuries among restrained vehicle occupants,” Tech. Rep., University of Maryland National Study Center for Trauma/EMS, 2001.
[4]  G. S. Pajenda, H. Seitz, M. Mousavi, and V. Vecsei, “Concomitant intra-abdominal injuries in pelvic trauma,” Wien Klin Wochenscher, vol. 110, no. 23, pp. 834–840, 1998.
[5]  Z. Balogh, K. L. King, P. Mackay et al., “The epidemiology of pelvic ring fractures: a population-based study,” Journal of Trauma, vol. 63, no. 5, pp. 1066–1072, 2007.
[6]  P. C. Ferrera and D. A. Hill, “Good outcomes of open pelvic fractures,” Injury, vol. 30, no. 3, pp. 187–190, 1999.
[7]  F. D. Brenneman, D. Katyal, B. R. Boulanger, M. Tile, and D. A. Redelmeier, “Long-term outcomes in open pelvic fractures,” Journal of Trauma, vol. 42, no. 5, pp. 773–777, 1997.
[8]  M. H. Moghari and P. Abolmaesumi, “Global registration of multiple bone fragments using statistical atlas models: feasibility experiments,” in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '08), pp. 5374–5377, August 2008.
[9]  M. H. Moghari and P. Abolmaesumi, “Global registration of multiple point sets: feasibility and applications in multi-fragment fracture fixation,” in Proceedings of 10th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '07), vol. 10, pp. 943–950, Brisbane, Australia, 2007.
[10]  S. Winkelbach, R. Westphal, and T. Goesling, “Pose estimation of cylindrical fragments for semi-automatic bone fracture reduction,” in Proceedings of the 25th Annual Symposium of the German Association for Pattern Recognition (DAGM '03), vol. 2781 of Lecture Notes in Computer Science, pp. 566–573, Magdeburg, Germany, 2003.
[11]  D. M. Ryder, S. L. King, C. J. Olliff, and E. Davies, “Possible method of monitoring bone fracture and bone characteristics using a non-invasive acoustic technique,” in Proceedings of the International Conference on Acoustic Sensing and Imaging, pp. 159–163, March 1993.
[12]  T. S. Douglas, V. Sanders, R. Pitcher, and A. B. van As, “Early detection of fractures with low-dose digital X-ray images in a pediatric trauma unit,” Journal of Trauma, vol. 65, no. 1, pp. E4–E7, 2008.
[13]  T. P. Tian, Y. Chen, W. K. Leow, W. Hsu, T. S. Howe, and M. A. Png, “Computing neck-shaft angle of femur for X-ray fracture detection,” in Proceedings of the International Conference on Computer Analysis of Images and Patterns, vol. 2756 of Lecture Notes in Computer Science, pp. 82–89, Springer, 2003.
[14]  V. L. F. Lum, W. K. Leow, Y. Chen, T. S. Howe, and M. A. Png, “Combining classifiers for bone fracture detection in X-ray images,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), pp. 1149–1152, September 2005.
[15]  C. Lee, S. Huh, T. A. Ketter, and M. Unser, “Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images,” Computers in Biology and Medicine, vol. 28, no. 3, pp. 309–338, 1998.
[16]  J. Montagnat and H. Delingette, “4D deformable models with temporal constraints: application to 4D cardiac image segmentation,” Medical Image Analysis, vol. 9, no. 1, pp. 87–100, 2005.
[17]  J. Schmid and N. Magnenat-Thalmann, “MRI bone segmentation using deformable models and shape priors,” in Proceedings of 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '08), vol. 11, pp. 119–126, New York, NY, USA, 2008.
[18]  P. C. T. Gon?alves, J. M. R. S. Tavares, and R. M. N. Jorge, “Segmentation and simulation of objects represented in images using physical principles,” Computer Modeling in Engineering and Sciences, vol. 32, no. 1, pp. 45–55, 2008.
[19]  S. Sandor and R. Leahy, “Surface-based labeling of cortical anatomy using a deformable atlas,” IEEE Transactions on Medical Imaging, vol. 16, no. 1, pp. 41–54, 1997.
[20]  W. Cai, S. Chen, and D. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognition, vol. 40, no. 3, pp. 825–838, 2007.
[21]  H. A. Vrooman, C. A. Cocosco, R. Stokking et al., “KNN-based multi-spectral MRI brain tissue classification: manual training versus automated atlas-based training,” in Medical Imaging 2006: Image Processing, Proceedings of the SPIE, San Diego, Calif, USA, February 2006.
[22]  J. Wu, P. Davuluri, K. Ward, C. Cockrell, R. Hobson, and K. Najarian, “A new hierarchical method for multi-level segmentation of bone in pelvic CT scans,” in Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '11), 2011.
[23]  S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509–522, 2002.
[24]  T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models-their training and application,” Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38–59, 1995.
[25]  F. Maes, D. Vandermeulen, and P. Suetens, “Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information,” Medical Image Analysis, vol. 3, no. 4, pp. 373–386, 1999.
[26]  G. P. Nason and B. W. Silverman, “The stationary wavelet transform and some statistical applications,” in Wavelets and Statistics, vol. 103 of Lecture Notes in Statistics, pp. 281–299, Springer, 1995.
[27]  N. Otsu, “A threshold selection method from gray level histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.

Full-Text

comments powered by Disqus