Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 1/2017

01-02-2017

Survey of Non-Rigid Registration Tools in Medicine

Authors: András P. Keszei, Benjamin Berkels, Thomas M. Deserno

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2017

Login to get access

Abstract

We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.
Literature
1.
Gering DT, Nabavi A, Kikinis R, et al: An integrated visualization system for surgical planning and guidance using image fusion and an open MR. J Magn Reson Imaging 13:967–975, 2001CrossRefPubMed
2.
Lehmann T, Gröndahl H, Benn D: Computer-based registration for digital subtraction in dental radiology. Dentomaxillofac Rad 29:323–346, 2000CrossRef
3.
Elhawary H, Oguro S, Tuncali K, et al: Multimodality non-rigid image registration for planning, targeting and monitoring during CT-guided percutaneous liver tumor cryoablation. Acad Radiol 17:1334–1344, 2010CrossRefPubMedPubMedCentral
4.
Huang X, Ren J, Guiraudon G, Boughner D, Peters TM: Rapid dynamic image registration of the beating heart for diagnosis and surgical navigation. IEEE Trans Med Imaging 28:1802–1814, 2009CrossRefPubMed
5.
Wein W, Brunke S, Khamene A, Callstrom MR, Navab N: Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12:577–585, 2008CrossRefPubMed
6.
Ganser KA, Dickhaus H, Metzner R, Wirtz CR: A deformable digital brain atlas system according to Talairach and Tournoux. Med Image Anal 8:3–22, 2004CrossRefPubMed
7.
Wu C, Murtha P, Jaramaz B: Femur statistical atlas construction based on two-level 3D non-rigid registration. Comput Aided Surg 14:83–99, 2009CrossRefPubMed
8.
9.
Foskey M, Davis B, Goyal L, et al: Large deformation three-dimensional image registration in image-guided radiation therapy. Phys Med Biol 50:5869–5892, 2005
10.
Dornheim L, Tönnies KD, Dixon K: Automatic segmentation of the left ventricle in 3D SPECT data by registration with a dynamic anatomic model in Medical Image Computing and Computer-Assisted Intervention--MICCAI 2005. Springer, Berlin Heidelberg, 2005, pp 335–342CrossRef
11.
Grosland NM, Bafna R, Magnotta VA: Automated hexahedral meshing of anatomic structures using deformable registration. Comput Methods Biomech Biomed Eng 12:35–43, 2009CrossRef
12.
Hill DL, Batchelor PG, Holden M, Hawkes DJ: Medical image registration. Phys Med Biol 46:R1–R45, 2001CrossRefPubMed
13.
14.
Slomka PJ, Baum RP: Multimodality image registration with software: state-of-the-art. Eur J Nucl Med Mol Imaging 36:44–55, 2009CrossRef
15.
Van den Elsen PA, Pol E-JD, Viergever MA: Medical image matching-a review with classification. IEEE Eng Med Biol 12:26–39, 1993CrossRef
16.
Oliveira FP, Tavares JMR: Medical image registration: a review. Comput Methods Biomech Biomed Eng 17:73–93, 2014CrossRef
17.
Penney GP, Weese J, Little JA, Desmedt P, Hill DL, Hawkes DJ: A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE Trans Med Imaging 17:586–595, 1998CrossRefPubMed
18.
19.
Holden M: A review of geometric transformations for nonrigid body registration. IEEE Trans Med Imaging 27:111–128, 2008CrossRefPubMed
20.
Makela T, Clarysse P, Sipila O, et al: A review of cardiac image registration methods. IEEE Trans Med Imaging 21:1011–1021, 2002CrossRefPubMed
21.
Gholipour A, Kehtarnavaz N, Briggs R, Devous M, Gopinath K: Brain functional localization: a survey of image registration techniques. IEEE Trans Med Imaging 26:427–451, 2007CrossRefPubMed
22.
Pearlman PC, Adams A, Elias SG, Willem PTM, Viergever MA, Pluim JP: Mono-and multimodal registration of optical breast images. J Biomed Opt 17:0809011–08090110, 2012CrossRef
23.
Liao R, Zhang L, Sun Y, Miao S, Chefd’Hotel C: A review of recent advances in registration techniques applied to minimally invasive therapy. IEEE Trans multimedia 15:983–1000, 2013CrossRef
24.
25.
Ramirez L, Durdle N, Raso V: Medical image registration in computational intelligence framework: a review. Canadian Conference on Electrical and Computer Engineering 2003 I.E. CCECE, 2003
26.
Shams R, Sadeghi P, Kennedy RA, Hartley RI: A survey of medical image registration on multicore and the GPU. IEEE Signal Proc Mag 27:50–60, 2010CrossRef
27.
Damas S, Cordón O, Santamaria J: Medical image registration using evolutionary computation: An experimental survey. IEEE Comput Intell Mag 6:26–42, 2011CrossRef
28.
Eklund A, Dufort P, Forsberg D, LaConte SM: Medical image processing on the GPU--Past, present and future. Med Image Anal 17:1073–1094, 2013CrossRefPubMed
29.
Fluck O, Vetter C, Wein W, Kamen A, Preim B, Westermann R: A survey of medical image registration on graphics hardware. Comp Meth Prog Bio 104:e45–e57, 2011CrossRef
30.
Bankman I: Handbook of medical image processing and analysis. Academic, San Diego, 2008
31.
32.
Goshtasby AA: 2-D and 3-D image registration: for medical, remote sensing, and industrial applications. John Wiley & Sons, Hoboken, 2005
33.
Hajnal J, Hill D, Hawkes D: Medical image registration. CRC Press, Boca Raton, 2001CrossRef
34.
35.
Fedorov A, Beichel R, Kalpathy-Cramer J, et al: 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341, 2012CrossRefPubMedPubMedCentral
36.
Hawkes DJ: Registration methodology: introduction in Medical Image Registration. CRC Press, New York, 2001, pp p. 11–p. 38CrossRef
37.
Zitova B, Flusser J: Image registration methods: a survey. Image Vision Comput 21:977–1000, 2003CrossRef
38.
Sotiras A, Ou Y, Glocker B, Davatzikos C, Paragios N: Simultaneous geometric-iconic registration in Medical Image Computing and Computer-Assisted Intervention--MICCAI 2010. Springer, Berlin Heidelberg, 2010, pp 676–683CrossRef
39.
Rueckert D, Schnabel JA: Medical image registration in Biomedical Image Processing. Springer, Berlin Heidelberg, 2011, pp 131–154
40.
Christensen GE, Johnson HJ: Consistent image registration. IEEE Trans Med Imaging 20:568–582, 2001CrossRefPubMed
41.
Roche A, Malandain G, Pennec X, Ayache N: The correlation ratio as a new similarity measure for multimodal image registration in Medical Image Computing and Computer-Assisted Interventation—MICCAI’98. Springer, Berlin Heidelberg, 1998, pp 1115–1124
42.
Astola J, Virtanen I: Entropy correlation coefficient, a measure of statistical dependence for categorized data. Proc Univ Vaasa Discussion paper 44, 1982
43.
Lo C-H, Don H-S: 3-D moment forms: their construction and application to object identification and positioning. IEEE Trans Pattern Anal 11:1053–1064, 1989CrossRef
44.
Shen D, Davatzikos C: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imaging 21:1421–1439, 2002CrossRefPubMed
45.
Glocker B, Komodakis N, Tziritas G, Navab N, Paragios N: Dense image registration through MRFs and efficient linear programming. Med Image Anal 12:731–741, 2008CrossRefPubMed
46.
Tustison NJ, Awate SP, Song G, Cook TS, Gee JC: Point Set Registration Using Havrda-Charvat-Tsallis Entropy Measures. IEEE Trans Med Imaging 30:451–460, 2011CrossRefPubMed
47.
He Y, Hamza AB, Krim H: A generalized divergence measure for robust image registration. IEEE Trans Signal Proces 51:1211–1220, 2003CrossRef
48.
Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P: Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198, 1997CrossRefPubMed
49.
Hill D, Batchelor PG: Registration methodology: Concepts and algorithms in Medical Image Registration. CRC Press, New York, 2001, pp p. 39–p. 70CrossRef
50.
Pluta J, Avants BB, Glynn S, Awate S, Gee JC, Detre JA: Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation. Hippocampus 19:565–571, 2009CrossRefPubMed
51.
Woods RP, Grafton ST, Holmes CJ, Cherry SR, Mazziotta JC: Automated image registration: I. General methods and intrasubject, intramodality validation. J Comput Assist Tomo 22:139–152, 1998CrossRef
52.
Ardekani BA, Guckemus S, Bachman A, Hoptman MJ, Wojtaszek M, Nierenberg J: Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. J Neurosci Meth 142:67–76, 2005CrossRef
53.
Alpert NM, Berdichevsky D, Levin Z, Morris ED, Fischman AJ: Improved methods for image registration. Neuroimage 3:10–18, 1996CrossRefPubMed
54.
Schwarz LA: Non-rigid Registration Using Free-form Deformations. Technische Universität München, 2007
55.
Papenberg N, Schumacher H, Heldmann S, et al: A fast and flexible image registration toolbox in Bildverarbeitung für die Medizin 2007. Springer, Berlin Heidelberg, 2007, pp 106–110CrossRef
56.
Beg MF, Miller MI, Trouve A, Younes L: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vision 61:139–157, 2005CrossRef
57.
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC: A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044, 2011CrossRefPubMed
58.
Klein A, Andersson J, Ardekani BA, et al: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46:786–802, 2009CrossRefPubMedPubMedCentral
59.
Murphy K, van Ginneken B, Reinhardt JM, et al: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans Med Imaging 30:1901–1920, 2011CrossRefPubMed
60.
Arganda-Carreras I, Sorzano CO, Marabini R, Carazo J: María, Ortiz-de-Solorzano C, Kybic J: Consistent and elastic registration of histological sections using vector-spline regularization in Computer Vision Approaches to Medical Image Analysis Lecture Notes in Computer Science. Springer, Berlin Heidelberg, 2006, pp 85–95
61.
Sorzano CO, Thevenaz P, Unser M: Elastic registration of biological images using vector-spline regularization. IEEE Trans Bio-Med Eng 52:652–663, 2005CrossRef
62.
Ou Y, Sotiras A, Paragios N, Davatzikos C: DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. Med Image Anal 15:622–639, 2011CrossRefPubMed
63.
Glocker B, Sotiras A, Komodakis N, Paragios N: Deformable medical image registration: Setting the state of the art with discrete methods. Annu Rev Biomed Eng 13:219–244, 2011CrossRefPubMed
64.
Klein S, Staring M, Murphy K, Viergever MA, Pluim JP: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29:196–205, 2010CrossRefPubMed
65.
Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18:712–721, 1999CrossRefPubMed
66.
Rueckert D: Nonrigid Registration: Concepts, Algorithms, and Applications in Medical Image Registration. CRC Press, New York, 2001, pp p. 281–p. 301
67.
Davis MH, Khotanzad A, Flamig DP, Harms SE: Elastic body splines: a physics based approach to coordinate transformation in medical image matching. Proceedings of the Eighth IEEE Symposium on Computer-Based Medical Systems, 1995
68.
Andersson J, Jenkinson M, Smith S: Non-linear registration, aka spatial normalisation. FMRIB technical report TR07JA2: FMRIB Centre, Oxford, United Kingdom, 2007
69.
Gilles B, Pai DK: Fast musculoskeletal registration based on shape matching in Medical Image Computing and Computer-Assisted Intervention--MICCAI 2008. Springer, Berlin Heidelberg, 2008, pp 822–829CrossRef
70.
Studholme C, Hill DL, Hawkes DJ: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32:71–86, 1999CrossRef
71.
Schnabel JA, Rueckert D, Quist M, et al: A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations in Medical Image Computing and Computer-Assisted Intervention--MICCAI 2001. Springer, Berlin Heidelberg, 2001, pp 573–581CrossRef
72.
Kroon DJ, Slump CH: MRI modalitiy transformation in demon registration. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009
73.
Thirion J-P: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260, 1998CrossRefPubMed
74.
Modat M, Ridgway GR, Taylor ZA, et al: Fast free-form deformation using graphics processing units. Comp Meth Prog Bio 98:278–284, 2010CrossRef
75.
76.
Vercauteren T, Pennec X, Perchant A, Ayache N: Symmetric log-domain diffeomorphic registration: A demons-based approach in Medical Image Computing and Computer-Assisted Intervention--MICCAI 2008. Springer, Berlin Heidelberg, 2008, pp 754–761CrossRef
77.
78.
Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier, Amsterdam, 2007
79.
80.
Friston KJ, Ashburner J, Frith CD, Poline JB, Heather JD, Frackowiak RSJ: Spatial registration and normalization of images. Hum Brain Mapp 3:165–189, 1995CrossRef
81.
Shackleford JA, Shusharina N, Verberg J, et al.: Plastimatch 1.6 — Current capabilities and future directions. In Proc. MICCAI 2012 Image-Guidance and Multimodal Dose Planning in Radiation Therapy Workshop, 108–119, 2012
82.
Woods RP, Grafton ST, Watson JD, Sicotte NL, Mazziotta JC: Automated image registration: II Intersubject validation of linear and nonlinear models. J Comput Assist Tomo 22:153–165, 1998CrossRef
83.
84.
Vercauteren T, Pennec X, Perchant A, Ayache N: Non-parametric diffeomorphic image registration with the demons algorithm. Medical Image Computing and Computer-Assisted Intervention- MICCAI 2007, Pt 2. Proceedings 4792:319–326, 2007
85.
Shen D: Image registration by local histogram matching. Pattern Recogn 40:1161–1172, 2007CrossRef
Metadata
Title
Survey of Non-Rigid Registration Tools in Medicine
Authors
András P. Keszei
Benjamin Berkels
Thomas M. Deserno
Publication date
01-02-2017
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 1/2017
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-016-9915-8

Other articles of this Issue 1/2017

Journal of Digital Imaging 1/2017 Go to the issue

App Review

Review of Radiology: App for Android by the Radiological Society of North America (RSNA)

OriginalPaper

Does Expectation of Abnormality Affect the Search Pattern of Radiologists When Looking for Pulmonary Nodules?

OriginalPaper

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks

OriginalPaper

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer

OriginalPaper

Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images

OriginalPaper

Cyber-Security Issues in Healthcare Information Technology