Abstract
Stroke is the third major cause of death worldwide behind heart disease and cancer. Carotid atherosclerosis is the most frequent cause of ischemic stroke. Early diagnosis of carotid plaque and serial monitoring of its size with the help of imaging modalities can help to prevent the atherosclerotic complications. The main difficulty is inevitable variability of patient’s head positions during image acquisitions. The time series registration of carotid images helps to improve the monitoring, characterization, and quantification of the disease by suppressing the global movements of the patient. In this work, a novel hybrid registration technique has been proposed and evaluated for registration of carotid ultrasound images taken at different times. The proposed hybrid method bridges the gap between the feature-based and intensity-based registration methods. The feature-based iterative closest point algorithm is used to provide a coarse registration which is subsequently refined by the intensity-based algorithm. The proposed framework uses rigid transformation model coupled with mutual information (MI) similarity measure and Powell optimizer. For quantitative evaluation, different registration approaches have been compared using four error metrics: visual information fidelity, structural similarity index, correlation coefficient, and MI. Qualitative evaluation has also been done using the visual examination of the registered image pairs.
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References
Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256
Biasiolli L, Noble JA, Robson MD (2010) Multicontrast MRI registration of carotid arteries in atherosclerotic and normal subjects. In: Dawant BM, Haynor DR (eds) Medical imaging 2010: image processing, SPIE 7623, Society of Photo-Optical Instrumentation Engineers, Bellingham. doi:10.1117/12.844510
Brott TG, Halperin JL, Abbara S, Bacharach JM, Barr JD, Bush RL, Cates CU, Creager MA, Fowler SB, Friday G, Hertzberg VS, McIff EB, Moore WS, Panagos PD, Riles TS, Rosenwasser RH, Taylor AJ (2011) ASA/ACCF/AHA/AANN/AANS/ACR/ASNR/CNS/SAIP/SCAI/SIR/SNIS/SVM/SVS guideline on the management of patients with extracranial carotid and vertebral artery disease. J Am Coll Cardiol 57(8):e16–e94
Cachier P, Bardinet E, Dormont D, Pennec X, Ayache N (2003) Iconic feature based nonrigid registration: the PASHA algorithm. Comput Vis Image Underst 89:272–298
Carvalho DDB, Klein S, Akkus Z, ten Kate GL, Tang H, Selwaness M, Schinkel AFL, Bosch JG, van der Lugt A, Niessen WJ (2012) Registration of free-hand ultrasound and MRI of carotid arteries through combination of point-based and intensity-based algorithms. In: Dawant BM, Christensen GE, Fitzpatrick JM, Rueckert D (eds) Proceedings of the 5th international workshop on Biomedical Image Registration (WBIR), Nashville, TN, USA, LNCS 7359, Springer Berlin, Heidelberg, pp 131–140. doi:10.1007/978-3-642-31340-0_14
Chan RC, Sokka S, Hinton D, Houser S, Manzke R, Hanekamp A, Reddy VY, Kaazempur-Mofrad MR, Rasche V (2006) Non-rigid registration for fusion of carotid vascular ultrasound and MRI volumetric datasets. In: Reinhardt JM, Pluim JPW (eds) Medical imaging 2006: image processing, SPIE 6144, Society of Photo-Optical Instrumentation Engineers, Bellingham, pp 772–779. doi:10.1117/12.651786
Chiu B, Shamdasani V, Entrekin R, Yuan C, Kerwin WS (2012) Characterization of carotid plaques on 3-dimensional ultrasound imaging by registration with multicontrast magnetic resonance imaging. J Ultrasound Med 31(10):1567–1580
Fayad ZA, Fuster V (2001) Clinical imaging of the high-risk or vulnerable atherosclerotic plaque. Circ Res 89:305–316
Fei B, Suri JS, Wilson DL (2005) Three-dimensional volume registration of carotid MR images. Stud Health Technol Inform 113:394–411
Gupta A, Verma HK, Gupta S (2012) Technology and research developments in carotid image registration. Biomed Signal Process Control 7(6):560–570
Hajnal JV, Hill DLG, Hawkes DJ (2001) Medical image registration. Biomedical engineering series. CRC Press LLC, Boca Raton
Hellier P, Barillot C (2003) Coupling dense and landmark-based approaches for nonrigid registration. IEEE Trans Med Imaging 22(2):217–227
Herzig R, Burval S, Krupka B, Vlachova I, Urbanek K, Mares J (2004) Comparison of ultrasonography, CT angiography, and digital subtraction angiography in severe carotid stenoses. Eur J Neurol 11:774–781
Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2):187–198
Nanayakkara ND, Chiu B, Samani A, Spence JD, Samarabandu J, Fenster A (2008) A twisting and bending model-based nonrigid image registration technique for 3-D ultrasound carotid images. IEEE Trans Med Imaging 27(10):1378–1388
Nanayakkara ND, Chiu B, Samani A, Spence JD, Samarabandu J, Parraga G, Fenster A (2009) Nonrigid registration of three-dimensional ultrasound and magnetic resonance images of the carotid arteries. Med Phys 36(2):373–385
Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1988) Numerical recipes in C: the art of scientific computing. Cambridge University Press, New York
Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15:430–444
Shields K, Barber DC, Sherriff SB (1993) Image registration for the investigation of atherosclerotic plaque movement. In: Barrett HH, Gmitro AF (eds) Proceedings of the 13th international conference on information processing in medical imaging (IPMI), LNCS 687, Springer, Flagstaff, Arizona, USA, pp 438–458. doi:10.1007/BFb0013804
Slomka PJ, Mandel J, Downey D, Fenster A (2001) Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries. Ultrasound Med Biol 27(7):945–955
Sotiras A, Ou Y, Glocker B, Davatzikos C, Paragios N (2010) Simultaneous geometric—iconic registration. In: Jiang T, Navab N, Pluim JPW, Viergever MA (eds) 13th international conference on medical image computing and computer assisted intervention (MICCAI), Part II, LNCS 6362, Springer, Beijing, China, pp 676–683
Viola P, Wells WM III (1997) Alignment by maximization of mutual information. Int J Comput Vis 24(2):137–154
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Acknowledgments
Authors are highly thankful to the radiologists Dr. Gurdeep Singh and Dr. Sandeep Singh Pawar from Advance Diagnostics, Ludhiana, for their valuable help in this work.
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Gupta, A., Verma, H.K. & Gupta, S. A hybrid framework for registration of carotid ultrasound images combining iconic and geometric features. Med Biol Eng Comput 51, 1043–1050 (2013). https://doi.org/10.1007/s11517-013-1086-x
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DOI: https://doi.org/10.1007/s11517-013-1086-x