Multiscale shape description of MR brain images using active contour models

Julia Anne Schnabel; Simon Robert Arridge

doi:10.1117/12.237962

16 April 1996 Multiscale shape description of MR brain images using active contour models

Julia Anne Schnabel, Simon Robert Arridge

Author Affiliations +

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237962
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

In this paper we present a hierarchical multiscale shape description tool based on active contour models which enables data-driven quantitative and qualitative shape studies of MR brain images at multiple scales. At large scales, global shape properties are extracted from the image, while smaller scale features are suppressed. At lower scales, the detailed shape characteristics become more prominent. Extracting a shape at different levels of scale yields a hierarchical multiscale shape stack. This shape stack can be used to localize and characterize shape changes like deformations and abnormalities at different levels of scale. The shape description is performed as a set of implicit segmentation steps at multiple scales yielding descriptions of an object at various levels of detail. Implicit segmentation is carried out using the well-known model of active contours. Starting from an initial active contour, several implicit optimization processes with differently regularized energy functions are performed, where the energy functions are represented as functions of scale. The presented algorithm for shape focusing and description based on active contour models shows promising results on extracting and characterizing complex shapes in MR brain images at a large set of scales.

Citation Download Citation

Julia Anne Schnabel and Simon Robert Arridge "Multiscale shape description of MR brain images using active contour models", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237962

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE