Movatterモバイル変換

9 May 2002Mixture of principal axes registration: a neural computation approach

Rujirutana Srikanchana, Jianhua Xuan,Kun Huang, Matthew T. Freedman M.D.,Yue Joseph Wang

Rujirutana Srikanchana,¹ Jianhua Xuan,¹ Kun Huang,¹ Matthew T. Freedman M.D.,² Yue Joseph Wang¹

¹The Catholic Univ. of America (United States)
²Georgetown Univ. Medical Ctr. (United States)

Proceedings Volume 4684, Medical Imaging 2002: Image Processing; (2002)https://doi.org/10.1117/12.467045
Event:Medical Imaging 2002, 2002, San Diego, California, United States

ARTICLE
CITED BY
- Google Scholar citations
- Citing works
DOWNLOAD PAPER SAVE TO MY LIBRARY

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: $335ADD TO CART

25 downloads per 1-year subscription

Members: $145

Non-members: $250ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members:ADD TO CART

This will count as one of your downloads.

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

DOWNLOAD NOW

Abstract

Non-rigid image registration is a prerequisite for many medical imaging applications such as change analysis in image-based diagnosis and therapy assessment. Nonlinear interpolation methods may be used to recover the deformation if the correspondence of the extracted feature points is available. However, it may be very difficult to establish such correspondence at an initial stage when confronted with large and complex deformation. In this paper, a mixture of principal axes registration (mPAR) is proposed to tackle the correspondence problem using a neural computation method. The feature is to align two point sets without needing to establish the explicit point correspondence. The mPAR aligns two point sets by minimizing the relative entropy between their probability distributions resulting in a maximum likelihood estimate of the transformation mixture. The neural computation for the mPAR is developed using a committee machine to obtain a mixture of piece-wise rigid registrations. The complete registration process consists of two steps: (1) using the mPAR to establish an improved point correspondence and (2) using a multilayer perceptron (MLP) neural network to recover the nonlinear deformation. The mPAR method has been applied to register a contrast-enhanced magnetic resonance (MR) image sequence. The experimental results show that our method not only improves the point correspondence but also results in a desirable error-resilience property for control point selection errors.

CitationDownload Citation

Rujirutana Srikanchana,Jianhua Xuan,Kun Huang,Matthew T. Freedman M.D., andYue Joseph Wang"Mixture of principal axes registration: a neural computation approach", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002);https://doi.org/10.1117/12.467045

ACCESS THE FULL ARTICLE