Registration of multimodality medical image using ordinary Procrustes analysis and maximum likelihood framework

Wanhyun Cho; Jonghyun Park; Sunworl Kim; Myungeun Lee; Soonhyoung Park; Junsik Lim; Gueesan Lee; Huy Phat Le; Soohyung Kim; Changbu Jeong

doi:10.1117/12.811851

27 February 2009 Registration of multimodality medical image using ordinary Procrustes analysis and maximum likelihood framework

Wanhyun Cho, Jonghyun Park, Sunworl Kim, Myungeun Lee, Soonhyoung Park, Junsik Lim, Gueesan Lee, Huy Phat Le, Soohyung Kim, Changbu Jeong

Author Affiliations +

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 72622M (2009) https://doi.org/10.1117/12.811851
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

We propose a new registration method that can do the alignment of two medical images using simultaneously the ordinary Procrustes analysis as well as a maximum likelihood framework with the EM algorithm. In an initial registration, we first extract the feature points representing the shape information from the boundary of the segmented object, and then we apply the ordinary Procrustes analysis to register exactly two sets of extracted feature points. For the final registration, we define a new alignment measure with the log-likelihood function derived by the Bayes theory and the maximum likelihood method with EM algorithm. In the E-step, we compute the posterior distribution of label variable by taking expectation for the log-likelihood function. And in the M-step, we derive the estimators for all parameters by maximizing the log-likelihood function. Then, we can optimize the transformation parameters for the final image registration by applying iteratively this measure. Finally, we conduct the various experiments to analyze the accuracy and precision of the proposed method. The experimental results show that our method has great potential power to register various images given by multimodality instruments.

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Wanhyun Cho, Jonghyun Park, Sunworl Kim, Myungeun Lee, Soonhyoung Park, Junsik Lim, Gueesan Lee, Huy Phat Le, Soohyung Kim, and Changbu Jeong "Registration of multimodality medical image using ordinary Procrustes analysis and maximum likelihood framework", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 72622M (27 February 2009); https://doi.org/10.1117/12.811851

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KEYWORDS

Image registration

Image segmentation

Expectation maximization algorithms

Computed tomography

Medical imaging

Magnetic resonance imaging

Feature extraction

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