Image segmentation by connected parametrical models

Marko Bukovec; Roel Truyen; Bostjan Likar; Rok Bernard; Franjo Pernus

doi:10.1117/12.533629

12 May 2004 Image segmentation by connected parametrical models

Marko Bukovec, Roel Truyen, Bostjan Likar, Rok Bernard, Franjo Pernus

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.533629
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Accurate, robust, and efficient automated segmentation of anatomical structures is difficult because of their complexity and inter-patient and inter-acquisition variations in shape, appearance, and spatial arrangement. To deal with the complexity and variations of anatomical structures, a number of model-based segmentation methods (e.g. snakes, shape and appearance models, image templates, etc.) have been proposed in the past. However, because applying individual models often does not lead to robust and accurate segmentation, we propose a framework for image segmentation by connected parametrical models. In our framework, which is a generalization of Fischler and Elschlager's pictorial structure (IEEE TC, 1973) and Bernard's et al. hierarchical principal component analysis framework (MICCAI, 2001), models cooperate via their parameters at different levels of hierarchy. In parametrical space, defined by parameters of all cooperating models, parameters may be connected by physical or statistical models. Connections may work either between intrinsic parameters of individual models, such as position, orientation and scale, or alternatively between arbitrarily transformed parameters, obtained, for instance, by PCA. By selecting appropriate parametrical models and models of connections between them, segmentation by the proposed framework has the potential to achieve accurate and robust results on a large variety of medical images. A framework, incorporating physical and/or statistical models of connections between intrinsic or transformed parameters is demonstrated on real images.

Citation Download Citation

Marko Bukovec, Roel Truyen, Bostjan Likar, Rok Bernard, and Franjo Pernus "Image segmentation by connected parametrical models", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.533629

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Statistical analysis

Statistical modeling

Principal component analysis

Modeling

Shape analysis

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years