Browsing by Author "Chen, Tai-Been"

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64-Bit and 128-bit DX random number generators
(2010-08-01)
Deng, Lih-Yuan
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Lu, Henry Horng-Shing
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Chen, Tai-Been
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統計學研究所
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Institute of Statistics
Extending 32-bit DX generators introduced by Deng and Xu (ACM Trans Model Comput Simul 13:299-309, 2003), we perform an extensive computer search for classes of 64-bit and 128-bit DX generators of large orders. The period lengths of these high resolution DX generators are ranging from 10(1915) to 10(58221). The software implementation of these generators can be developed for 64-bit or 128-bit hardware. The great empirical performances of DX generators have been confirmed by an extensive battery of tests in the TestU01 package. These high resolution DX generators can be useful to perform large scale simulations in scientific investigations for various computer systems.
Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT
(2014-03-01)
Chen, Tai-Been
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Lu, Henry Horng-Shing
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Kim, Hang-Keun
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Son, Young-Don
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Cho, Zang-Hee
State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time. (C) 2013 Elsevier Ltd. All rights reserved.
MicroPET reconstruction with random coincidence correction via a joint Poisson model
(2008-07-01)
Chen, Tai-Been
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Chen, Jyh-Cheng
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Lu, Henry Horng-Shing
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Liu, Ren-Shyan
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統計學研究所
Positron emission tomography (PET) can provide in vivo, quantitative and functional information for diagnosis; however, PET image quality depends highly on a reconstruction algorithm. Iterative algorithms, such as the maximum likelihood expectation maximization (MLEM) algorithm, are rapidly becoming the standards for image reconstruction in emission-computed tomography. The conventional MLEM algorithm utilized the Poisson model in its system matrix, which is no longer valid for delay-subtraction of randomly corrected data. The aim of this study is to overcome this problem. The maximum likelihood estimation using the expectation maximum algorithm (MLE-EM) is adopted and modified to reconstruct microPET images using random correction from joint prompt and delay sinograms; this reconstruction method is called PDEM. The proposed joint Poisson model preserves Poisson properties without increasing the variance (noise) associated with random correction. The work here is an initial application/demonstration without applied normalization, scattering. attenuation, and arc correction. The coefficients of variation (CV) and full width at half-maximum (FWHM) values were utilized to compare the quality of reconstructed microPET images of physical phantoms acquired by filtered backprojection (FBP), ordered subsets-expected maximum (OSEM) and PDEM approaches. Experimental and simulated results demonstrate that the proposed PDEM produces better image quality than the FBP and OSEM approaches. (C) 2007 IPEM. Published by Elsevier Ltd. All rights reserved.
Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction
(2020-10-01)
Huang, Guan-Hua
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Lin, Chih-Hsuan
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Cai, Yu-Ren
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Chen, Tai-Been
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Hsu, Shih-Yen
We analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over-sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state-of-the-art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.
Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation
(2012)
Chen, Tai-Been
;
Chen, Jyh-Cheng
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Lu, Henry Horng-Shing
;
統計學研究所
;
Institute of Statistics
Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.
Segmentation of cDNA microarray images by kernel density estimation
(2008-12-01)
Chen, Tai-Been
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Lu, Henry Horng-Shing
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Lee, Yun-Shien
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Lan, Hsiu-Jen
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統計學研究所
The segmentation of cDNA microarray spots is essential in analyzing the intensities of microarray images for biological and medical investigation. In this work, nonparametric methods using kernel density estimation are applied to segment two-channel cDNA microarray images. This approach groups pixels into both a foreground and a background. The segmentation performance of this model is tested and evaluated with reference to 16 microarray data. In particular, spike genes with various contents are spotted in a microarray to examine and evaluate the accuracy of the segmentation results. Duplicated design is implemented to evaluate the accuracy of the model. The results of this study demonstrate that this method can cluster pixels and estimate statistics regarding spots with high accuracy. (c) 2008 Elsevier Inc. All rights reserved.
STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING
(2012-06-01)
Chueh, Tung-Hung
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Chen, Tai-Been
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Lu, Henry Horng-Shing
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Ju, Shan-Shan
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Tao, Teh-Ho
For the importance of communication between human and machine interface, it would be valuable to develop an implement which has the ability to recognize emotional states. In this paper, we proposed an approach which can deal with the daily dependence and personal dependence in the data of multiple subjects and samples. 30 features were extracted from the physiological signals of subject for three states of emotion. The physiological signals measured were: electrocardiogram (ECG), skin temperature (SKT) and galvanic skin response (GSR). After removing the daily dependence and personal dependence by the statistical technique of MANOVA, six machine learning methods including Bayesian network learning, naive Bayesian classification, SVM, decision tree of C4.5, Logistic model and K-nearest-neighbor (KNN) were implemented to differentiate the emotional states. The results showed that Logistic model gives the best classification accuracy and the statistical technique of MANOVA can significantly improve the performance of all six machine learning methods in emotion recognition system.
String-averaging expectation-maximization for maximum likelihood estimation in emission tomography
(2014-05-01)
Helou, Elias Salomao
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Censor, Yair
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Chen, Tai-Been
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Chern, I-Liang
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De Pierro, Alvaro Rodolfo
We study the maximum likelihood model in emission tomography and propose a new family of algorithms for its solution, called string-averaging expectation maximization (SAEM). In the string-averaging algorithmic regime, the index set of all underlying equations is split into subsets, called \'strings\', and the algorithm separately proceeds along each string, possibly in parallel. Then, the end-points of all strings are averaged to form the next iterate. SAEM algorithms with several strings present better practical merits than the classical row-action maximum-likelihood algorithm. We present numerical experiments showing the effectiveness of the algorithmic scheme, using data of image reconstruction problems. Performance is evaluated from the computational cost and reconstruction quality viewpoints. A complete convergence theory is also provided.