Abstract
Despite recent advances in the field of image-guided interventions (IGI), the bottleneck for Angiography/X-ray guided procedures in particular is accurate and robust 2D-3D image alignment. The conventional, straight-forward parameter optimization approach is known to be ill-posed and less efficient. Retrieval-based approaches may be of superior choice here. However, this requires salient and robust image features, which can handle the difficulties of Angiographic images such as high level of noise and contrast variance. In this paper, we investigate state-of-the-art features of the field of computer vision regarding the applicability and reliability in the challenging scenario of Angiography.
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References
Peters T, Cleary K., Image-Guided Interventions: Technology and Applications. Springer Science & Business Media; 2008.
Gallagher AG, Kearney PP, McGlade KJ, et al. Avoidable factors can compromise image-guided interventions. Medscape Gen Surg. 2012.
DeLucia PR, Mather RD, Griswold JA, et al. Toward the improvement of imageguidedinterventions for minimally invasive surgery: three factors that affect performance. Hum Factors. 2006;48(1):23–38.
Markelj P, Tomaževič D, Likar B, et al. A review of 3D/2D registration methods for image-guided interventions. Med Image Anal. 2012;16(3):642–61.
Aksoy T, Unal GB, Demirci S, et al. Template-based CTA to X-ray angio rigid registration of coronary arteries in frequency domain with automatic X-ray segmentation. Med Phys. 2013;40(10).
Mitrović U, Pernuš F, Likar B, et al. Simultaneous 3D–2D image registration and c-arm calibration: application to endovascular image-guided interventions. Med Phys. 2015;42(11):6433–47.
Schulte zu Berge C, Grunau A, Mahmud H, et al. CAMPVis—A Game Engineinspired Research Framework for Medical Imaging and Visualization. Technische Universität München; 2014.
Frangi AF, Niessen WJ, Vincken KL, et al. Multiscale vessel enhancement filtering. Proc MICCAI. 1998; p. 130–7.
Aylward SR, Bullitt E. Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Med Imaging. 2002;21(2):61–75.
Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.
Bay H, Tuytelaars T, Van Gool L. Surf: speeded up robust features. Proc ECCV. 2006; p. 404–17.
Calonder M, Lepetit V, Strecha C, et al. Brief: binary robust independent elementary features. Proc ECCV. 2010; p. 778–92.
Rublee E, Rabaud V, Konolige K, et al.; IEEE. ORB: an efficient alternative to SIFT or SURF. Proc ICCV. 2011; p. 2564–71.
Levi G, Hassner T. LATCH: learned arrangements of three patch codes. arXiv preprint arXiv:150103719. 2015.
Chen L, Xiang Y, Chen Y, et al.; IEEE. Retinal image registration using bifurcation structures. Proc ICIP. 2011; p. 2169–72.
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Hofschen, K., Geissler, T., Rieke, N., Berge, C., Navab, N., Demirci, S. (2016). Image Descriptors in Angiography. In: Tolxdorff, T., Deserno, T., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2016. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49465-3_50
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DOI: https://doi.org/10.1007/978-3-662-49465-3_50
Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-49465-3
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