Registration of 3D fetal brain US and MRI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7511 LNCS, Issue , 2012, Pages 667-674

  Murgasova, Maria Kuklisova ^a   Cifor, Amalia ^a   Napolitano, Raffaele ^b   Papageorghiou, Aris ^b   Quaghebeur, Gerardine ^b   Alison Noble, J ^a   Schnabel, Julia A ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b JOHN RADCLIFFE HOSPITAL   (United Kingdom)

Author keywords

Block matching; Fetal 3D ultrasound; Fetal ultrasound template; Ultrasound and MR registration

Indexed keywords

BRAIN; IMAGE SEGMENTATION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING; MOTION COMPENSATION; ULTRASONICS;

3-D ULTRASOUND; BLOCK MATCHING; FETAL ULTRASOUND; GOOD CORRELATIONS; MISSING FEATURES; PROBABILISTIC ATLAS; ULTRASOUND IMAGES; ULTRASOUND VOLUME;

IMAGE RECONSTRUCTION;

EID: 84988861995     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33418-4_82     Document Type: Conference Paper

References (11)

1. Rajagopalan, V., Scott, J., Habas, P.A., Kim, K., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: Spatiotemporal Morphometry of Adjacent Tissue Layers with Application to the Study of Sulcal Formation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 476–483. Springer, Heidelberg (2011)
2. Arbel, T., Morandi, X., Comeau, R.M., Louis Collins, D.: Automatic Non-linear MRI-Ultrasound Registration for the Correction of Intra-operative Brain Deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 913–922. Springer, Heidelberg (2001)
3. Roche, A., Pennec, X., Malandain, G., Ayache, N.: Rigid registration of 3D ultrasound with MR images: A new approach combining intensity and gradient information. IEEE Transactions on Medical Imaging 20(10), 1038–1049 (2001)
4. Monteagudo, A., Timor-Tritsch, I.E.: Normal sonographic development of the central nervous system from the second trimester onwards using 2D, 3D and transvaginal sonography. Prenatal Diagnosis 29(4), 326–339 (2009)
5. Wein, W., Brunke, S., Khamene, A., Callstrom, M.R., Navab, N.: Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Medical Image Analysis 12, 577–585 (2008)
6. Ourselin, S., Roche, A., Subsol, G., Pennec, X., Ayache, N.: Reconstructing a 3D structure from serial histological sections. Image and Vision Computing 19(12), 25–31 (2001)
7. Murgasova, M., Quaghebeur, G., Hajnal, J.V., Schnabel, J.A.: Unified framework for superresolution reconstruction of 3D fetal brain mr images from 2D slices with intensity correction and outlier rejection. In: MICCAIWorkshop on Image Analysis of Human Brain Development, IAHBD (2011)
8. Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated modelbased bias field correction of MR images of the brain. IEEE TMI 18(10), 885–896 (1999)
9. Kuklisova-Murgasova M., Aljabar, P., Srinivasan, L., Counsell, S.J., Doria, V., Serag, A., Gousias, I.S., Boardman J.P., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: A dynamic 4D probabilistic atlas of the developing brain. NeuroImage 54(4), 2750–2763 (2011)
10. Hashimoto, K., Shimizu, T., Shimoya, K., Kanzaki, T., Clapp, J.F., Murata, Y.: Fetal cerebellum: US appearance with advancing gestational age. Radiology 221(1), 70–74 (2001)
11. Rousseeuw, P.J.: Least median of squares regression. Journal of the American Statistical Association 79(388), 871–880 (1984)