Abstract
An improvement of the Active Shape procedure introduced by Cootes and Taylor is presented. The new automated brain segmentation and interpretation approach incorporates a priori knowledge about neuroanatomic structures and their specific structural relationships to provide robust segmentation and labeling.
The method was trained in 8 MR brain images and tested in 19 brain images by comparison to observer-defined independent standards. Neuroanatomic structures in all images from the test set were successfully identified. The presented method is applicable to virtually any task involving deformable shape analysis.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
T F Cootes, A Hill, C J Taylor, and J Haslam. Use of active shape models for locating structures in medical images. Image & Vision Computing, 12(6):355–366, 1994.
T F Cootes, C J Taylor, D H Cooper, and J Graham. Active shape models — their training and application. Computer Vision and Image Understanding, 61:38–59, 1995.
M Sonka, S K Tadikonda, and S M Collins. Knowledge-based interpretation of MR brain images. IEEE Trans. Med. Imaging, 15:443–452, 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duta, N., Sonka, M. (1997). Segmentation and interpretation of MR brain images using an improved knowledge-based active shape model. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_29
Download citation
DOI: https://doi.org/10.1007/3-540-63046-5_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63046-3
Online ISBN: 978-3-540-69070-2
eBook Packages: Springer Book Archive