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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain

Author(s): Saadia Binte Alam*, Manabu Nii, Akinobu Shimizu and Syoji Kobashi

Volume 16, Issue 5, 2020

Page: [499 - 506] Pages: 8

DOI: 10.2174/1573405615666181120141147

Price: $65

Abstract

Background: This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time.

Aims: Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by Estep using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject’s age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter.

Methods: The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean ± SD = 59.32±16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2.

Results: We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer’s disease (AD) identification utilizing support vector machine.

Conclusion: In this study, st-SSM based adult brain shape feature extraction and classification techniques are introduced to classify between normal and AD subject as an application.

Keywords: Spatiotemporal statistical shape model, brain, magnetic resonance imaging, shape analysis, age, Alzheimer's disease identification.

Graphical Abstract

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