Abstract
Image guided Surgery (IGS) has become familiar on enhancing it with computerized technologies which makes processing much easier. These computerized systems are highly sophisticated and improvised which orients the surgeon to perform the surgery with three dimensional images of the patients MR images. This work presents the Non Rigid Registration of Brain MRI with the help of Cloud computing. There exist geometric ambiguities among individuals due to the varying shape of the cortex and this is a challenging task when it comes to registration process. Intensity based registration is not alone enough to tackle the issues hence anatomical knowledge is highly important. Segmentation and labelling of cortex sulci with non-parametric approach that enables capturing of its shape and topology. The intensity and feature points are matched using the registration energy. A linear combination of compact smooth linear field and branched basic function is formed. The addition of the sulci from the upper border till its bottom part makes the registration process even more efficient. Cloud computing has been used in two scenarios, Firstly, the complete workflow of registration is enabled at the cloud to handle hypothetical computations and obtain precise results. Secondly, storage and computational nodes are used to handle large amount number of images nearly 7TB. The Deformable registration is evaluated and its feasibility has been analysed using cloud resources which resulted in timely execution of complex components during registration. The result obtained has proved that Cloud Computing provides practical and cost effective support for telemedicine while performing Image guided Surgery. The accuracy level has been improved upto 62% while performing Non Rigid Registration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jolesz FA (2014) Intraoperative imaging and image-guided therapy. Springer, Berlin
Liu Y. Kot A, Drakopoulos F, Yao C, Fedorov A, Enquobahrie A, Clatz O, Chrisochoides NP (2014) An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. Front Neuroinformat 8(33)
Fedorov A, Clifford B, Warfield S, Kikinis R, Chrisochoides N (2009) Non-rigid registration for image-guided neurosurgery on the teragrid: a case study. Technical report WM-CS-2009-05, Department of Computer Science
Delingette H, Ayache N (2004) Soft tissue modeling for surgery simulation, vol XII of hand book of numerical analysis: special volume: computational models for the human body, 1st edn. Elsevier, Netherlands, pp 453–550
Skerl D, Likar B, Pernuˇs F (2008) A protocol for evaluation of similarity measures for nonrigid registration. Med Image Anal 12(1):42–54
Ino F, Kawasaki Y, Tashiro T, Nakajima Y, Sato Y, Tamura S, Hagihara K (2006) A parallel implementation of 2-d/3-d image registration for computer-assisted surgery. Int J Bioinformat Res Appl 2(4):341–357
Gannon D, Fay D, Green D, Takeda K, Yi W (2014) Science in the cloud: lessons from three years of research projects on microsoft azure. In: Proceedings of the 5th ACM workshop on scientific cloud computing, Science Cloud ’14. ACM, New York, pp 1–8
Mell PM, Grance T (2011) Sp 800–145. The NIST definition of cloud computing. Technical report, Gaithersburg, MD, United States
Foster I (2002) What is the grid? A three point checklist, white paper. http://wwwfp.mcs.anl.gov/~foster/Articles/WhatIsTheGrid.pdf
MammoGrid Project (2008) http://www.cems.uwe.ac.uk/cccs/project.php?name=mammogrid
Biomedical Informatics Research Network (2008). http://www.nbirn.net
Dong S, Insley J, Karonis NT, Papka ME, Binns J, Karniadakis G (2006) Simulating and visualizing the human arterial system on the TeraGrid. Future Gener Comput Syst 22:1011–1017
Manos S, Zasada S, Mazzeo MD, Haines R, Doctors G, Brew S, Pinning R, Brooke J, Coveney PV (2008) Patient specific whole cerebral blood flow simulation: a future role in surgical treatment for neurovascular pathologies. In: Proceedings of Teragrid’08
Archip N, Clatz O, Whalen S, Kacher D, Fedorov A, Kot A, Chrisochoides N, Jolesz F, Golby A, Black PM, Warfield SK (2007) Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery. Neuroimage 35:609–624
Chrisochoides N, Fedorov A, Kot A, Archip N, Clatz O, Kikinis R, Warfield SK (2006) Toward real-time image guided neurosurgery using distributed and grid computing. In: Proceedings of the ACM/IEEE Conference on Supercomputing
Majumdar A, Birnbaum A, Choi DJ, Trivedi A, Warfield SK, Baldridge K, Krysl P (2005) A dynamic data driven grid system for intra-operative image guided neurosurgery. Proc ICCS 2005:672–679
Clatz O, Delingette H, Talos IF, Golby AJ, Kikinis R, Jolesz FA, Ayache N, Warfield SK (2005) Robust non-rigid registration to capture brain shift from intra-operative MRI. IEEE Tran Med Imag 24(11):1417–1427
Foteinos P, Chrisochoides N (2014) High quality real-time Imageto-Mesh conversion for finite element simulations. J. Parallel Distrib Comput 74(2):2123–2140
Liu Y, Foteinos P, Chernikov A, Chrisochoides N (2012) Mesh deformation-based multi-tissue mesh generation for brain images. J Eng Comput 29(4):305–318
Drakopoulos F, Foteinos P, Liu Y, Chrisochoides NP (2014) Toward a real time multi-tissue adaptive physics-based nonrigid registration framework for brain tumor resection. Front Neuroinformat 8(11)
Vangel M, Fedorov A, W. W. III, Tempany C (2012) Statistical framework for characterization of deformable registration performance. Tech. report, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Commandeur F, Velut J, Acosta O (2011) A VTK algorithm for the computation of the hausdorff distance. VTK J
Microsoft (2013) “Microsoft azure for research overview”. http://research.microsoft.com/enus/projects/azure/windows-azure-for-research-overview.pdf
Talos IF, Archip N (2007) Volumetric non-rigid registration for MRI-guided brain tumor surgery, Tech. Report, Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Kraut A, Moretti S, Robinson-Rechavi M, Stockinger H, Flanders D (2010) Phylogenetic code in the cloud–can it meet the expectations? Stud Health Technol Informat 159:55–63
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Preetha Evangeline, D., Anandhakumar, P. (2019). Non-rigid Registration of Brain MR Images for Image Guided Neurosurgery Using Cloud Computing. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-04061-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04060-4
Online ISBN: 978-3-030-04061-1
eBook Packages: EngineeringEngineering (R0)