Medical image registration in image guided surgery: Issues, challenges and research opportunities

• Autorzy: Alam F. , Rahman S. U. , Ullah S. , Gulati K.

Identyfikatory

DOI

10.1016/j.bbe.2017.10.001

• Języki publikacji: EN

Abstrakty

EN

Multimodal images of a patient obtained at different time, pre-surgical planning, intra-procedural guidance and visualization, and post-procedural assessment are the core components of image-guided surgery (IGS). In IGS, the goal of registration is to integrate corresponding information in different images of the same organ into a common coordinate system. Registration is a fundamental task in IGS and its main purpose is to provide better visualization and navigation to the surgeons. In this paper, we describe the most popular types of medical image registration and evaluate their prominent state-of-the art issues and challenges in image-guided surgery. We have also presented the factors which affect the accuracy, reliability and efficiency of medical image registration methods. It is not possible to achieve highly successful IGS until all the issues and challenges in registration process are identified and subsequently solved.

Słowa kluczowe

EN

medical image registration; image registration methods; image guided surgery

PL

rejestracja obrazu medycznego; metoda rejestracji obrazu; obraz operacji medycznej

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 1
• Strony: 71--89
• Opis fizyczny: Bibliogr. 86 poz., rys.

Twórcy

autor

Alam F.

• Department of Computer Science & IT, University of Malakand, Dir (L), Khyber Pakhtunkhwa, Pakistan

autor

Rahman S. U.

• Department of Computer Science & IT, University of Malakand, Dir (L), Khyber Pakhtunkhwa, Pakistan

autor

Ullah S.

• Department of Computer Science & IT, University of Malakand, Dir (L), Khyber Pakhtunkhwa, Pakistan

autor

Gulati K.

• School of Computer Science and Information Technology, Stratford University, VA, USA

Bibliografia

• [1] Cleary K, Peters TM. Image-guided interventions: technology review and clinical applications. Annu Rev Biomed Eng 2010;12:119–42.
• [2] Lindseth F, Langø T, Selbekk T, Hansen R, Reinertsen I, Askeland C, et al. Ultrasound-based guidance and therapy. In: Gunarathne GPP, editor. Advancements and breakthroughs in ultrasound imaging. 2013.
• [3] Castro Pareja CR. Real-time 3D elastic image registration. The Ohio State University; 2004.
• [4] Bali RK. Clinical knowledge management: opportunities and challenges. Idea Group Pub.; 2005.
• [5] Bankman I. Handbook of medical imaging: processing and analysis management. Elsevier Science; 2000.
• [6] Miga MI, Clements LW, Galloway RL, Miga MI, Clements LW, Galloway RL. Apparatus and methods of compensating for organ deformation, registration of internal structures to images, and applications of same. Google Patents; 2008.
• [7] Olver PJ, Tannenbaum A. Mathematical methods in computer vision. Springer; 2003.
• [8] Thompson S, Penney G, Billia M, Challacombe B, Hawkes D, Dasgupta P. Design and evaluation of an image-guidance system for robot-assisted radical prostatectomy. BJU Int 2013;111:1081–90.
• [9] Alam F, Rahman SU, Khusro S, Ullah S, Khalil A. Evaluatin of medical image registration techniques based on nature and domain of the transformation. J Med Imaging Radiat Sci 2016;47:178–93.
• [10] Dogra A, Patterh MS. CT and MRI brain images registration for clinical applications. Cancer Sci Ther 2014;6:18–26.
• [11] Alam F, Rahman SU, Khalil A, Khusro S, Sajjad M. Deformable registration methods for medical images: a review based on performance comparison. Proc Pak Acad Sci A: Phys Comput Sci 2016;53:111–30.
• [12] Alam F, Rahman SU, Khalil A, Ullah S, Khusro S. Quantitative evaluation of intrinsic registration methods for medical images. Sindh Univ Res J – SURJ (Sci Ser) 2017;491:43–8.
• [13] Alam F, Rahman SU, Ullah S, Khalil A, Uddin A. A review on extrinsic registration methods for medical images. Tech J Univ Eng Technol Taxila 2016;21:110–9.
• [14] Alam F, Rahman SU. Intrinsic registration techniques for medical images: a state-of-the-art review. J Postgrad Med Inst (Peshawar-Pakistan) 2016;30.
• [15] Zitova B, Flusser J. Image registration methods: a survey. Image Vis Comput 2003;21:977–1000.
• [16] Gonzalez RC. Digital image processing. Pearson Education; 2009.
• [17] DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Image-guided intraoperative cortical deformation recovery using game theory: application to neocortical epilepsy surgery. IEEE Trans Med Imaging 2010;29:322–38.
• [18] Risholm P, Golby AJ, Wells WM. Multi-modal image registration for pre-operative planning and image guided neurosurgical procedures. Neurosurg Clin N Am 2011;22:197–206.
• [19] Csapo I, Davis B, Shi Y, Sanchez M, Styner M, Niethammer M. Longitudinal image registration with non-uniform appearance change. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer; 2012. p. 280–8.
• [20] Patel PM, Shah VM. Image registration techniques: a comprehensive survey. Int J Innov Res Dev 2014;3:68–78.
• [21] El-Samie FEA, Hadhoud MM, El-Khamy SE. Image super-resolution and applications. CRC Press; 2012.
• [22] Goshtasby AA. 2-D and 3-D image registration: for medical, remote sensing, and industrial applications. Wiley; 2005.
• [23] Deng H, University OS. Image feature detection and matching for biological object recognition. Oregon State University; 2007.
• [24] Navab N, Jannin P.Information Processing in Computer- Assisted Interventions: First International Conference, IPCAI 2010. Proceedings. Springer; 2010.
• [25] Zhao Q, Pizer S, Niethammer M, Rosenman J. Geometric-feature-based spectral graph matching in pharyngeal surface registration. Medical Image Computing and Computer-Assisted Intervention: MICCAI. International Conference on Medical Image Computing and Computer- Assisted Intervention, vol. 17. 2014. pp. 259–66.
• [26] Feng DD. Biomedical information technology. Elsevier Science; 2011.
• [27] Yankeelov TE, Pickens DR, Price RR. Quantitative MRI in cancer. CRC Press; 2011.
• [28] Maurer C, Maciunas RJ, Fitzpatrick JM. Registration of head CT images to physical space using a weighted combination of points and surfaces [image-guided surgery]. IEEE Trans Med Imaging 1998;17:753–61.
• [29] Shamir RR, Joskowicz L, Shoshan Y. Fiducial optimization for minimal target registration error in image-guided neurosurgery. IEEE Trans Med Imaging 2012;31:725–37.
• [30] Otake Y, Armand M, Sadowsky O, Armiger RS, Kazanzides P, Taylor RH. An iterative framework for improving the accuracy of intraoperative intensity-based 2D/3D registration for image-guided orthopedic surgery. In: Navab N, Jannin P, editors. Information Processing in Computer- Assisted Interventions: First International Conference, IPCAI 2010. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 23–33.
• [31] Lindseth F, Langø T, Selbekk T, Hansen R, Reinertsen I, Askeland C, et al. Ultrasound-based guidance and therapy. Advancements and Breakthroughs in Ultrasound Imaging. 2013. pp. 28–82.
• [32] Wen P. Medical image registration based-on points, contour and curves. 2008 International Conference on BioMedical Engineering and Informatics. 2008. pp. 132–6.
• [33] Chou Y-Y. Transitive and symmetric nonrigid image registration. Citeseer; 2004.
• [34] Meyer J. Histogram transformation for inter-modality image registration. 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. 2007. pp. 1118–23.
• [35] Hajnal JV, Hill DLG. Medical image registration. CRC Press; 2001.
• [36] Beutel J. Handbook of medical imaging: medical image processing and analysis. Society of Photo Optical; 2000.
• [37] Wilson D, Laxminarayan S. Handbook of biomedical image analysis: vol. 3: Registration models. Kluwer Academic/ Plenum Publishers; 2007.
• [38] Lee M-E, Kim S-H, Seo I-H. Intensity-based registration of medical images. 2009 International Conference on Test and Measurement. 2009. pp. 239–42.
• [39] Guo Y. Medical image registration and application to atlas-based segmentation. Kent State University; 2007.
• [40] Erdt M, Steger S, Sakas G. Regmentation: a new view of image segmentation and registration. J Radiat Oncol Inform 2012;4:1–23.
• [41] Rivera T, Uruchurtu E. Radiation monitoring in interventional cardiology: a requirement. J Phys: Conf Ser 2017;012098.
• [42] Lisle DA. Imaging for students. 4th ed. CRC Press; 2012.
• [43] Gingold E. Modern fluoroscopy imaging systems. Image Wisely; 2014.
• [44] Sra J. Cardiac image registration. J Atrial Fibrillation 2008;1 (September–November):25.
• [45] El Hakimi W. Accurate 3D-reconstruction and-navigation for high-precision minimal-invasive interventions. Technische Universität; 2016.
• [46] Otake Y, Schafer S, Stayman J, Zbijewski W, Kleinszig G, Graumann R, et al. Automatic localization of vertebral levels in X-ray fluoroscopy using 3D–2D registration: a tool to reduce wrong-site surgery. Phys Med Biol 2012;57:5485.
• [47] Fagan T, Truong U, Jone P, Bracken J, Quaife R, Hazeem A, et al. Multimodality 3-dimensional image integration for congenital cardiac catheterization. Methodist Debakey Cardiovasc J 2014;10:68–76. This study gives an overview on how fusion imaging can be used for planning and performing interventions in patients with congenital heart disease.
• [48] Rivest-Henault D, Sundar H, Cheriet M. Nonrigid 2D/3D registration of coronary artery models with live fluoroscopy for guidance of cardiac interventions. IEEE Trans Med Imaging 2012;31:1557–72.
• [49] Suntharos P, Setser RM, Bradley-Skelton S, Prieto LR. Real-time three dimensional CT and MRI to guide interventions for congenital heart disease and acquired pulmonary vein stenosis. Int J Cardiovasc Imaging 2017;1–8.
• [50] Narayan SA, Qureshi S. Multimodality medical image fusion: applications in congenital cardiology. Future Medicine; 2017.
• [51] van den Berg JC. Update on new tools for three-dimensional navigation in endovascular procedures. AORTA J 2014;2:279.
• [52] Markelj P, Tomaževič D, Likar B, Pernuš F. A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 2012;16:642–61.
• [53] Alam F, Rahman SU, Hassan M, Khalil A. An investigation towards issues and challenges in medical image registration. J Postgrad Med Inst (Peshawer Pakistan) 2017;31:224–33.
• [54] Liu Y. On the real-time performance, robustness and accuracy of medical image non-rigid registration. College of William & Mary; 2011.
• [55] Neri E, Baert AL, Caramella D, Bartolozzi C. Image processing in radiology: current applications. Springer; 2007.
• [56] Svedlow M, McGillem C, Anuta PE. Experimental examination of similarity measures and preprocessing methods used for image registration. LARS Symposia. 1976. p. 150.
• [57] Suehling M, Huber M, Soza G. Method and system for semantics driven image registration. Google Patents; 2012.
• [58] Keyvanpour M-R, Alehojat S. Analytical comparison of learning based methods to increase the accuracy and robustness of registration algorithms in medical imaging. Int J Adv Sci Technol 2012;41.
• [59] Russakoff DB, Tomasi C, Rohlfing T, Maurer Jr CR. Image similarity using mutual information of regions. European Conference on Computer Vision. 2004. pp. 596–607.
• [60] Pluim JPW, Maintz JBA, Viergever MA. Image registration by maximization of combined mutual information and gradient information. In: Delp SL, DiGoia AM, Jaramaz B, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000: Third International Conference. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg; 2000. p. 452–61.
• [61] Rueckert D, Clarkson MJ, Hill DLG, Hawkes DJ. Non-rigid registration using higher-order mutual information; 2000;438–47.
• [62] Yi Z, Soatto S. Nonrigid registration combining global and local statistics. IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. 2009. pp. 2200–7.
• [63] Loeckx D, Slagmolen P, Maes F, Vandermeulen D, Suetens P. Nonrigid image registration using conditional mutual information. IEEE Trans Med Imaging 2010;29:19–29.
• [64] Zhuang X, Arridge S, Hawkes DJ, Ourselin S. A nonrigid registration framework using spatially encoded mutual information and free-form deformations. IEEE Trans Med Imaging 2011;30:1819–28.
• [65] Myronenko A, Song X.Image registration by minimization of residual complexityIEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. 2009. pp. 49–56.
• [66] Woo J, Stone M, Prince JL. Multimodal registration via mutual information incorporating geometric and spatial context. IEEE Trans Image Process 2015;24:757–69.
• [67] Kaur A, Kaur L, Gupta S. Image recognition using coefficient of correlation and structural similarity index in uncontrolled environment. Int J Comput Appl 2012;59.
• [68] Kim J. Intensity based image registration using robust similarity measure and constrained optimization: applications for radiation therapy. Citeseer; 2004.
• [69] Bailey DL, Townsend DW, Valk PE, Maisey MN. Positron emission tomography: basic sciences. London: Springer; 2006.
• [70] Collignon A, Maes F, Delaere D, Vandermeulen D, Suetens P, Marchal G. Automated multi-modality image registration based on information theory. Information Processing in Medical Imaging. 1995. pp. 263–74.
• [71] Wells WM, Viola P, Atsumi H, Nakajima S, Kikinis R. Multi-modal volume registration by maximization of mutual information. Med Image Anal 1996;1:35–51.
• [72] Sabuncu MR. Entropy-based image registration. Citeseer; 2004.
• [73] Kim YS, Lee JH, Ra JB. Multi-sensor image registration based on intensity and edge orientation information. Pattern Recognit 2008;41:3356–65.
• [74] Liu X, Lei Z, Yu Q, Zhang X, Shang Y, Hou W. Multi-modal image matching based on local frequency information. EURASIP J Adv Signal Process 2013;2013:1–11.
• [75] Riegler G, Urschler M, M, R., ther, Bischof H, Stern D. Anatomical landmark detection in medical applications driven by synthetic data. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). 2015. pp. 85–9.
• [76] Qin B, Gu Z, Sun X, Lv Y. Registration of images with outliers using joint saliency map. IEEE Signal Process Lett 2010;17:91–4.
• [77] Auer M, Regitnig P, Holzapfel GA. An automatic nonrigid registration for stained histological sections. IEEE Trans Image Process 2005;14:475–86.
• [78] Likar B, Pernuš F. A hierarchical approach to elastic registration based on mutual information. Image Vis Comput 2001;19:33–44.
• [79] Tomazevic D, Likar B, Pernus F. 3-D/2-D registration by integrating 2-D information in 3-D. IEEE Trans Med Imaging 2006;25:17–27.
• [80] Staring M, Van Der Heide UA, Klein S, Viergever MA, Pluim JP. Registration of cervical MRI using multifeature mutual information. IEEE Trans Med Imaging 2009;28:1412–21.
• [81] Pszczolkowski S, Zafeiriou S, Ledig C, Rueckert D. A robust similarity measure for nonrigid image registration with outliers. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). 2014. pp. 568–71.
• [82] Kosiński W, Michalak P, Gut P. Robust image registration based on mutual information measure. J Signal Inf Process 2012;3:175.
• [83] Xuan Y, Jihong P. Elastic image registration using attractive and repulsive particle swarm optimization. In: Wang T-D, Li X, Chen S-H, Wang X, Abbass H, Iba H, et al., editors. Simulated Evolution and Learning: 6th International Conference, SEAL 2006. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg; 2006. p. 782–9.
• [84] Fitzpatrick JM. The role of registration in accurate surgical guidance. Proc Inst Mech Eng Part H: J Eng Med 2010;224:607–22.
• [85] Crum WR, Hartkens T, Hill D. Non-rigid image registration: theory and practice. Br J Radiol 2014;77(s2):S140–253.
• [86] Lüders H, Comair YG. Epilepsy surgery. Lippincott Williams & Wilkins; 2001.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).