Ms. Tungyou Lin Profile

Tungyou Lin

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 27 March 2009 Paper

Gene to mouse atlas registration using a landmark-based nonlinear elasticity smoother

Tungyou Lin, Carole Le Guyader, Erh-Fang Lee, Ivo Dinov, Paul Thompson, Arthur Toga, Luminita Vese

Proceedings Volume 7259, 72592Q (2009) https://doi.org/10.1117/12.812491

KEYWORDS: Image registration, Data modeling, Brain, Image segmentation, Composites, Medical imaging, Associative arrays, Algorithm development, Brain mapping, Genetics

Read Abstract +

We propose a unified variational approach for registration of gene expression data to neuroanatomical mouse atlas in two dimensions. The proposed energy (minimized in the unknown displacement u) is composed of three terms: a standard data fidelity term based on L² similarity measure, a regularizing term based on nonlinear elasticity (allowing larger smooth deformations), and a geometric penalty constraint for landmark matching. We overcome the difficulty of minimizing the nonlinear elasticity functional by introducing an auxiliary variable v that approximates ∇u, the Jacobian of the unknown displacement u. We therefore minimize now the functional with respect to the unknowns u (a vector-valued function of two dimensions) and v (a two-by-two matrix-valued function). An additional quadratic term is added, to insure good agreement between v and ∇u. In this way, the nonlinearity in the derivatives of the unknown u no longer exists in the obtained Euler-Lagrange equations, producing simpler implementations. Several satisfactory experimental results show that gene expression data are mapped to a mouse atlas with good landmark matching and smooth deformation. We also present comparisons with the biharmonic regularization. An advantage of the proposed nonlinear elasticity model is that usually no numerical correction such as regridding is necessary to keep the deformation smooth, while unifying the data fidelity term, regularization term, and landmark constraints in a single minimization approach.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years