Hybrid boundary-based and region-based deformable models for biomedical image segmentation

John M. Gauch; Homer H. Pien; Jayant Shah

doi:10.1117/12.179272

8 July 1994 Hybrid boundary-based and region-based deformable models for biomedical image segmentation

John M. Gauch, Homer H. Pien, Jayant Shah

Proceedings Volume 2299, Mathematical Methods in Medical Imaging III; (1994) https://doi.org/10.1117/12.179272
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

The problem of segmenting an image into visually sensible regions has received considerable attention. Recent techniques based on deformable models show particular promise for this problem because they produce smooth closed object boundaries. These techniques can be broadly classified into two categories: boundary-based deformable models, and region-based deformable models. Both of these approaches have distinct advantages and disadvantages. In this paper, we introduce a hybrid deformable modeling technique which combines the advantages of both approaches and avoids some of their disadvantages. This is accomplished by first minimizing a region-based functional to obtain initial edge strength estimates. Smooth closed object boundaries are then obtained by minimizing a boundary-based functional which is attracted to the initial edge locations. In this paper, we discuss the theoretical advantages of this hybrid approach over existing image segmentation methods and show how this technique can be effectively implemented and used for the segmentation of 2D biomedical images.

Citation Download Citation

John M. Gauch, Homer H. Pien, and Jayant Shah "Hybrid boundary-based and region-based deformable models for biomedical image segmentation", Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); https://doi.org/10.1117/12.179272

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

Includes PDF, HTML & Video, when available