Abstract
The correct diagnosis of brain diseases is crucial for children with brain disorders. But the complex characteristics of infant brain make the image analysis very complicated. Thus, an accurate image registration is a prerequisite for accurate analysis of MR infant brain images, and it provides valuable information for the diagnosis of doctors. This paper presents our research works on SURF registration algorithm of 2-D MR infant brain images. We firstly describe the original algorithm and analyze its advantages and drawbacks. Then an improved version is proposed, which uses 8-D descriptor vectors with the length of 128. The experiment results show, compared with the original version, our algorithm can achieve more accurate image registration with a little more time consumption. For all the images tested, the increase of correct matching rate varies from a minimum of 5.7 % to a maximum of 14.9 % compared with the classical one.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rueekert, D., Sonoda, L., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)
Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)
Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 1150–1157 (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Kumar, P., Henikoff, S., Ng, P.C.: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4(7), 1073–1081 (2009)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. Comput. Vis. Pattern Recogn. 1, 511–518 (2001)
Lindeberg, T.: Scale-space for discrete signals. Pattern Anal. Mach. Intell. 12(3), 234–254 (1990)
Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 79–116 (1998)
Zhu, Y., Cheng, S., Stanković, V., Stanković, L.: Image registration using BP-SIFT. J. Vis. Commun. Image Represent. 24(4), 448–457 (2013)
Juan, L., Gwun, O.: A comparison of SIFT, PCA-SIFT and SURF. Image Process. 3(4), 143–152 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Du, K., Domas, S., Lenczner, M., Zhang, G. (2016). An Improved Algorithm Based on SURF for MR Infant Brain Image Registration. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-42294-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42293-0
Online ISBN: 978-3-319-42294-7
eBook Packages: Computer ScienceComputer Science (R0)