Abstract
Comparative radiography has a crucial role in the forensic identification endeavor. A proposal to automate the comparison of ante-mortem and post-mortem radiographs has been recently proposed based on an evolutionary image registration method. It considers the use of differential evolution to estimate the parameters of a 3D-2D registration transformation that automatically superimposes a bone surface model over a radiograph of the same bone. The main drawback of this proposal is the high computational cost. This contribution tackled this high computational cost by incorporating multi-resolution and multi-start strategies into its optimization process. We have studied the accuracy, robustness and computation time of the different configurations of the proposed method with synthetic images of patellae, clavicles and frontal sinuses. A significant improvement has been obtained in comparison to the state-of-the-art method in term of the robustness of the optimization method and computational cost with a drop in accuracy smaller than the 0.5% of the pixels of the silhouette of the bone or cavity.
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Notes
- 1.
The materials for generating this dataset were provided by Physical Anthropology Lab at the University of Granada and the Hospital de Castilla la Mancha.
References
Thali, M.J., Brogdon, B., Viner, M.D.: Forensic Radiology. CRC Press, Boca Raton (2002)
Kahana, T., Hiss, J.: Identification of human remains: forensic radiology. J. Clin. Forensic Med. 4(1), 7–15 (1997)
Markelj, P., Tomaževič, D., Likar, B., Pernuš, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2012)
Gómez, O., Ibáñez, O., Valsecchi, A., Cordón, O., Kahana, T.: 3D–2D silhouette-based image registration for comparative radiography-based forensic identification. Pattern Recogn. 83, 469–480 (2018)
Valsecchi, A., Damas, S., Santamaria, J.: Evolutionary intensity-based medical image registration: a review. Curr. Med. Imaging Rev. 9(4), 283–297 (2013)
Damas, S., et al.: Forensic identification by computer-aided craniofacial superimposition: a survey. ACM Comput. Surv. 43(4), 1–27 (2011)
Christensen, A.M.: Testing the reliability of frontal sinuses in positive identification. J. Forensic Sci. 50(1), 18–22 (2005)
Stephan, C.N., Amidan, B., Trease, H., Guyomarc’h, P., Pulsipher, T., Byrd, J.E.: Morphometric comparison of clavicle outlines from 3D bone scans and 2D chest radiographs: a shortlisting tool to assist radiographic identification of human skeletons. J. Forensic Sci. 59(2), 306–313 (2014)
Niespodziewanski, E., Stephan, C.N., Guyomarc’h, P., Fenton, T.W.: Human identification via lateral patella radiographs: a validation study. J. Forensic Sci. 61(1), 134–140 (2016)
Caple, J., Byrd, J., Stephan, C.N.: Elliptical fourier analysis: fundamentals, applications, and value for forensic anthropology. Int. J. Legal Med. 131(6), 1675–1690 (2017)
Russakoff, D.B., et al.: Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D–3D image registration. IEEE Trans. Med. Imaging 24(11), 1441–1454 (2005)
van de Kraats, E.B., Penney, G.P., Tomazevic, D., Van Walsum, T., Niessen, W.J.: Standardized evaluation methodology for 2-D-3-D registration. IEEE Trans. Med. Imaging 24(9), 1177–1189 (2005)
Feldmar, J., Ayache, N., Betting, F.: 3D–2D projective registration of free-form curves and surfaces. In: Fifth International Conference on Computer Vision, Proceedings, pp. 549–556. IEEE (1995)
Damas, S., Cordón, O., Santamaría, J.: Medical image registration using evolutionary computation: an experimental survey. IEEE Comput. Intell. Mag. 6(4), 26–42 (2011)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons. Kongelige Danske Videnskabernes Selskab 5, 1–34 (1948)
Quatrehomme, G., Fronty, P., Sapanet, M., Grévin, G., Bailet, P., Ollier, A.: Identification by frontal sinus pattern in forensic anthropology. Forensic Sci. Int. 83(2), 147–153 (1996)
Stephan, C., Winburn, A., Christensen, A., Tyrrell, A.: Skeletal identification by radiographic comparison: blind tests of a morphoscopic method using antemortem chest radiographs. J. Forensic Sci. 56(2), 320–332 (2011)
Bontrager, K.L., Lampignano, J.: Textbook of Radiographic Positioning and Related Anatomy. Elsevier Health Sciences (2013)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
Mery, D.: Computer Vision for X-Ray Testing. Springer, Cham (2015)
The CGAL Project, CGAL User and Reference Manual, 4.9.1 Edition, CGAL Editorial Board (2017)
Valsecchi, A., Damas, S., Santamaria, J., Marrakchi-Kacem, L.: Genetic algorithms for voxel-based medical image registration. In: IEEE Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI), pp. 22–29. IEEE (2013)
Page, M., Taylor, J., Blenkin, M.: Uniqueness in the forensic identification sciences-fact or fiction? Forensic Sci. Int. 206(1), 12–18 (2011)
Acknowledgements
Mr. Gómez’s work was supported by Spanish MECD FPU grant [grant number FPU14/02380].
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Gómez, O., Ibáñez, O., Valsecchi, A., Cordón, O. (2019). Improving Comparative Radiography by Multi-resolution 3D-2D Evolutionary Image Registration. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_9
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