Abstract
The C-arm X-ray system is a common intraoperative imaging modality used to observe the state of a fractured bone in orthopedic surgery. Using C-arm, the bone fragments are aligned during surgery, and their lengths and angles with respect to the entire bone are measured to verify the fracture reduction. Since the field-of-view of the C-arm is too narrow to visualize the entire bone, a panoramic X-ray image is utilized to enlarge it by stitching multiple images. To achieve X-ray image stitching with feature detection, the extraction of accurate and densely matched features within the overlap region between images is imperative. However, since the features are highly affected by the properties and sizes of the overlap regions in consecutive X-ray images, the accuracy and density of matched features cannot be guaranteed. To solve this problem, a heterogeneous stitching of X-ray images was proposed. This heterogeneous stitching was completed according to the overlap region based on homographic evaluation. To acquire sufficiently matched features within the limited overlap region, integrated feature detection was used to estimate a homography. The homography was then evaluated to confirm its accuracy. When the estimated homography was incorrect, local regions around the matched feature were derived from integrated feature detection and substituted to re-estimate the homography. Successful X-ray image stitching of the C-arm was achieved by estimating the optimal homography for each image. Based on phantom and ex-vivo experiments using the proposed method, we confirmed a panoramic X-ray image construction that was robust compared to the conventional methods.
Similar content being viewed by others
Availability of Data and Material
Not applicable.
Code Availability
Not applicable.
Abbreviations
- AP:
-
Anteroposterior
- BRISK:
-
Binary robust invariant scalable key points
- LAT:
-
Lateral
- MSAC:
-
M-estimator sample consensus
- RANSAC:
-
Random sample consensus
- SIFT:
-
Scale-invariant feature transform
- SSD:
-
Sum-of-square differences
- SURF:
-
Speed-up robust features
References
Wang L, et al.: Long bone X-ray image stitching using C-arm motion estimation. Informatik aktuell:202,2009
Yaniv Z, Joskowicz L: Long bone panoramas from fluoroscopic X-ray images. IEEE transactions on medical imaging 23:26-35,2004
Messmer P, Matthews F, Wullschleger C, Hügli R, Regazzoni P, Jacob AL: Image fusion for intraoperative control of axis in long bone fracture treatment. European Journal of Trauma 32:555-561,2006
Bai L, Yang JX, Chen XH, Sun YX, Li XY: Medical robotics in bone fracture reduction surgery: a review. Sensors 19,2019
Gaston P, Will EM, Keating JF: Recovery of knee function following fracture of the tibial plateau. Journal of Bone and Joint Surgery-British Volume 87B:1233-1236,2005
Dagnino G, et al.: Image-guided surgical robotic system for percutaneous reduction of joint fractures. Annals of biomedical engineering 45:2648-2662,2017
Chen C, Kojcev R, Haschtmann D, Fekete T, Nolte L, Zheng GY: Ruler based automatic C-arm image stitching without overlapping constraint. Journal of Digital Imaging 28:474-480,2015
Capek M, Wegenkittl R, Felkel P: A fully-automatic stitching of 2D medical data sets, 2002
Wang L, et al.: Long bone x-ray image stitching using camera augmented mobile c-arm. Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention: City
Wang L, Traub J, Weidert S, Heining SM, Euler E, Navab N: Parallax-free long bone X-ray image stitching. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention 12:173-180,2009
Szeliski R: Image alignment and stitching: Springer, 2006
Zhang Y, Zhou H: Image stitching based on particle swarm and maximum mutual information algorithm. Journal of Multimedia 8:580,2013
Kumar A, Bandaru RS, Rao BM, Kulkarni S, Ghatpande N: Automatic image alignment and stitching of medical images with seam blending. World Academy of Science, Engineering and Technology 65,2012
Salbiah S, Somaya A, Arof H, Saleh Z, Ibrahim F: A new approach to medical image stitching using minimum average correlation energy filter and peak to side‐lobe ratio. International Journal of Imaging Systems and Technology 22:166-171,2012
Maintz JA, Viergever MA: A survey of medical image registration. Medical image analysis 2:1-36,1998
Oliveira FP, Tavares JMR: Medical image registration: a review. Computer methods in biomechanics and biomedical engineering 17:73-93,2014
Zitova B, Flusser J: Image registration methods: a survey. Image and Vision Computing 21:977-1000,2003
Santini S, Jain R: Similarity measures. Ieee Transactions on Pattern Analysis and Machine Intelligence 21:871-883,1999
Chalom E, Asa E, Biton E: Measuring image similarity: an overview of some useful applications. Ieee Instrumentation & Measurement Magazine 16:24-28,2013
Kim J, Fessler JA: Intensity-based image registration using robust correlation coefficients. Ieee Transactions on Medical Imaging 23:1430-1444,2004
Mellor M, Brady M: Phase mutual information as a similarity measure for registration. Medical Image Analysis 9:330-343,2005
Yang F, He Y, Deng ZS, Yan A: Improvement of automated image stitching system for DR X-ray images. Computers in Biology and Medicine 71:108-114,2016
Briechle K, Hanebeck UD: Template matching using fast normalized cross correlation. Proceedings of SPIE:95–102,2001
Pei SC, Lin CN: Image normalization for pattern recognition. Image and Vision Computing 13:711-723,1995
Bardera A, Feixas M, Boada I: Normalized similarity measures for medical image registration. Proceedings of SPIE:108–118,2004
Mahalanobis A, Kumar B, Casasent D: Minimum average correlation energy filters. Applied Optics 26:3633-3640,1987
Kumar B, Savvides M, Xie C: Correlation pattern recognition for face recognition. Proceedings of the Ieee 94:1963-1976,2006
Singla S, Sharma R: Medical image stitching using hybrid of sift & surf techniques. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) 3:838-842,2014
Jing X, Zhenjiang M: An improved algorithm on image stitching based on SIFT features. Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), Innovative Computing, Information and Control, 2007 ICICIC '07 Second International Conference on:453,2007
Gong J-H, Zhang J-H, An Z-Z, Zhao W-W, Liu H-M: An approach for X-ray image mosaicing based on Speeded-Up Robust Features. Proc. 2012 International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP): City
Karami E, Prasad S, Shehata M: Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:171002726, 2017
Dare P, Dowman I: A new approach to automatic feature based registration of SAR and SPOT images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 33:125,2000
Bay H, Ess A, Tuytelaars T, Van Gool L: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110:346-359,2008
Leutenegger S, Chli M, Siegwart RY: BRISK: Binary Robust invariant scalable keypoints. 2011 International Conference on Computer Vision, Computer Vision (ICCV), 2011 IEEE International Conference on:2548–2555,2011
Alcantarilla PF, Bartoli A, Davison AJ, Alcantarilla PF, Bartoli A, Davison AJ: KAZE features, 2012
Alcantarilla PF, Nuevo J, Bartoli A: Fast explicit diffusion for accelerated features in nonlinear scale spaces. BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, 2013
Ha HG, Jeon S, Lee S, Choi H, Hong J: Perspective pinhole model with planar source for augmented reality surgical navigation based on C-arm imaging. International Journal of Computer Assisted Radiology and Surgery 13:1671-1682,2018
Vandewalle P, Süsstrunk S, Vetterli M: A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP journal on advances in signal processing 2006:071459,2006
Foroosh H, Zerubia JB, Berthod M: Extension of phase correlation to subpixel registration. IEEE transactions on image processing 11:188-200,2002
Case courtesy of A.Prof Frank Gaillard, http://radiopaedia.org., rID: 23325. Available at http://radiopaedia.org., rID: 23325.
Mikolajczyk K, Schmid C: A performance evaluation of local descriptors. Ieee Transactions on Pattern Analysis and Machine Intelligence 27:1615-1630,2005
Miksik O, Mikolajczyk K: Evaluation of local detectors and descriptors for fast feature matching. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Pattern Recognition (ICPR), 2012 21st International Conference on:2681–2684,2012
Tareen SAK, Saleem Z: A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK. 2018 International Conference on Computing, Mathematics and Engineering Technologies: Invent, Innovate and Integrate for Socioeconomic Development, iCoMET 2018 - Proceedings 2018-January:1, 2018
Funding
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A1A01064673).
Author information
Authors and Affiliations
Contributions
Ho-Gun Ha: conceptualization, methodology, writing—original draft. Kyunghwa Jung: conceptualization, methodology. Seongpung Lee: methodology. HyunKi Lee: conceptualization. Jaesung Hong: conceptualization, writing—reviewing and editing, supervision.
Corresponding author
Ethics declarations
Ethics Approval
Not applicable.
Consent to Participate
The authors agree to participate in this research study.
Consent for Publication
The authors agree to publish this research study.
Additional Declarations
Under the terms of use, we modified the uploaded X-ray images of femur from http://radiopaedia.org and utilized them as the test images in Fig. 3a, b. Under the terms of use from http://radiopaedia.org: for non-commercial, you can copy contents and alter and/or build upon it.
Conflict of Interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ha, HG., Jung, K., Lee, S. et al. Heterogeneous Stitching of X-ray Images According to Homographic Evaluation. J Digit Imaging 34, 1249–1263 (2021). https://doi.org/10.1007/s10278-021-00503-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-021-00503-9