Shape complexes in continuous max-flow segmentation

John S. H. Baxter; Jing Yuan; Maria Drangova; Terry M. Peters; Jiro Inoue

doi:10.1117/12.2216258

21 March 2016 Shape complexes in continuous max-flow segmentation

John S. H. Baxter, Jing Yuan, Maria Drangova, Terry M. Peters, Jiro Inoue

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978434 (2016) https://doi.org/10.1117/12.2216258
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Optimization-based segmentation approaches deriving from discrete graph-cuts and continuous max-flow have become increasingly nuanced, allowing for topological and geometric constraints on the resulting segmentation while retaining global optimality. However, these two considerations, topological and geometric, have yet to be combined in a unified manner. This paper presents the concept of shape complexes, which combine geodesic star convexity with extendable continuous max-flow solvers. These shape complexes allow more complicated shapes to be created through the use of multiple labels and super-labels, with geodesic star convexity governed by a topological ordering. These problems can be optimized using extendable continuous max-flow solvers. Previous work required computationally expensive co-ordinate system warping which are ill-defined and ambiguous in the general case. These shape complexes are validated in a set of synthetic images as well as atrial wall segmentation from contrast-enhanced CT. Shape complexes represent a new, extendable tool alongside other continuous max-flow methods that may be suitable for a wide range of medical image segmentation problems.

Citation Download Citation

John S. H. Baxter, Jing Yuan, Maria Drangova, Terry M. Peters, and Jiro Inoue "Shape complexes in continuous max-flow segmentation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978434 (21 March 2016); https://doi.org/10.1117/12.2216258

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