Abstract
In Digital Subtraction Angiography (DSA) image registration algorithm, the precision of the control points as well as their number and the distribution in image determine the accuracy of geometric correction and registration. Control points usually adopt the grid points; however, a more effective method is to extract control points adaptively according to the image feature. In this paper, a control point’s selection algorithm of DSA images is proposed based on adaptive multi-Scale vascular enhancement, error diffusion and means shift algorithms. Experimental results show that the proposed algorithm can adaptively put the control points to blood vessels and other key image characteristics, and can optimize the number of control points according to practical needs, which will ensure the accuracy of DSA image registration.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Schuldhaus, D., Spiegel, M., Redel, T., Polyanskaya, M., Struffert, T., Hornegger, J., Doerfler, A.: Classification-based summation of cerebral digital subtraction angiography series for image post-processing algorithms. Physics in Medicine and Biology 56(1), 1791–1802 (2011)
Sang, N., Li, H., Peng, W., Zhang, T.: Knowledge-based adaptive threshold segmentation of digital subtraction angiography images. Image and Vision Computing 25(8), 1263–1270 (2007)
Cebral, R., Castro, A., Appanaboyina, S., Putman, M., Millan, D., Frangi, F.: Efficient Pipeline for Image-Based Patient-Specific Analysis of Cerebral Aneurysm Hemodynamics: Technique and Sensitivity. IEEE Transactions on Medical Image 24(4), 457–467 (2005)
Zwiggelaar, R., Astley, M., Boggis, R., Taylor, J.: Linear Structures in Mammographic Images: Detection and Classification. IEEE Transactions on Medical Image 23(9), 1077–1086 (2004)
Floyd, R., Steinberg, L.: An adaptive algorithm for spatial gray-scale. Proceeding Society Information Display 17(2), 75–78 (1976)
Goldschneider, J., Riskin, E., Wong, P.: Embedded multilevel error diffusion. IEEE Transactions on Image Processing 6(7), 956–964 (1997)
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Information Theory 21(1), 32–40 (1975)
Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 17(8), 790–799 (1995)
Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: IEEE Int. Conf. Computer Vision, vol. 1, pp. 438–445 (2001)
Jimenez-Alaniz, J., Pohl-Alfaro, M., Medina-Banuelos, V., Yanez-Suarez, O.: Segmenting brain MRI using adaptive mean shift. In: 28th International IEEE EMBS Annual Conference, pp. 3114–3117 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, F., Li, C., Kong, S., Liu, S., Cui, Y. (2014). Mean Shift Based Feature Points Selection Algorithm of DSA Images. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-06269-3_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06268-6
Online ISBN: 978-3-319-06269-3
eBook Packages: Computer ScienceComputer Science (R0)