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

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