Curvature and shape variance based landmark tagging methods for building statistical object models

Sylvia Rueda; Jayaram K. Udupa; Li Bai

doi:10.1117/12.812452

13 March 2009 Curvature and shape variance based landmark tagging methods for building statistical object models

Sylvia Rueda, Jayaram K. Udupa, Li Bai

Proceedings Volume 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling; 726129 (2009) https://doi.org/10.1117/12.812452
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Model-based segmentation approaches, such as those employing Active Shape Models (ASMs), have proved to be useful for medical image segmentation and understanding. To build the model, however, we need an annotated training set of shapes wherein corresponding landmarks are identified in every shape. Manual positioning of landmarks is a tedious, time consuming, and error prone task, and almost impossible in the 3D space. In an attempt to overcome some of these drawbacks, we have devised several automatic methods under two approaches: c-scale based and shape variance based. The c-scale based methods use the concept of local curvature to find landmarks on the mean shape of the training set. These landmarks are then propagated to all the shapes of the training set to establish correspondence in a local-to-global manner. The variance-based method is guided by the strategy of equalization of the shape variance contained in the training set for selecting landmarks. The main premise here is that this strategy itself takes care of the correspondence issue and at the same time deploys landmarks very frugally and optimally considering shape variations. The desired landmarks are positioned around each contour so as to equally distribute the total variance existing in the training set in a global-to-local manner. The methods are evaluated on 40 MRI foot data sets and compared in terms of compactness. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced methods of annotation, and the variance equalization method tops the list.

Citation Download Citation

Sylvia Rueda, Jayaram K. Udupa, and Li Bai "Curvature and shape variance based landmark tagging methods for building statistical object models", Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 726129 (13 March 2009); https://doi.org/10.1117/12.812452

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

Atrial fibrillation

3D modeling

Medical imaging

3D image processing

Binary data

Bismuth

Show All Keywords

Keywords/Phrases

Search In:

Publication Years