scholarly journals Spatiotemporal Statistical Shape Model Construction for the Observation of Temporal Change in Human Brain Shape

2018 ◽  
Author(s):  
Saadia Binte Alam ◽  
Syoji Kobashi
Keyword(s):  
Human Brain ◽  
Temporal Change ◽  
Statistical Shape Model ◽  
Model Construction ◽  
Shape Model ◽  
Statistical Shape ◽  
Brain Shape

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

2020 ◽  
Vol 16 (5) ◽  
pp. 499-506
Author(s):  
Saadia Binte Alam ◽  
Manabu Nii ◽  
Akinobu Shimizu ◽  
Syoji Kobashi
Keyword(s):  
Weight Function ◽  
Shape Change ◽  
Gaussian Function ◽  
Adult Brain ◽  
Statistical Shape Model ◽  
Support Vector ◽  
Shape Model ◽  
Statistical Shape ◽  
Brain Shape ◽  
Time Point

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.

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