Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images

Elguebaly, Tarek; Bouguila, Nizar

doi:10.1007/978-3-642-12159-3_19

Cited by 32 publications

(13 citation statements)

References 24 publications

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“…We based our learning algorithm on the Monte Carlo simulation technique of Gibbs sampling, mixed with a Metropolis-Hasting step. 18 We used the integrated or marginal likelihood using the Laplace-Metropolis estimator to obtain M . 19 We applied our approach to images from the Iris thermal face database, which is a subset of the 'Object tracking and classification beyond the visible spectrum' database.…”

Section: Figure 1 Visual and Thermal-image Characteristics Taken Frmentioning

confidence: 99%

Enhanced IR face recognition using Bayesian unsupervised algorithm

Elguebaly¹,

Bouguila²

2010

SPIE Newsroom

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Section: Figure 1 Visual and Thermal-image Characteristics Taken Frmentioning

confidence: 99%

Enhanced IR face recognition using Bayesian unsupervised algorithm

Elguebaly¹,

Bouguila²

2010

SPIE Newsroom

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“…The importance of this field of research is highlighted by the publication of many papers for more than 20 years. [16][17][18][19]26,27,29,32,54,67 For instance, the classification of MRI images into normal or abnormal has been broadly studied in the literature and several interesting articles presenting various methods have been published. 26 A CNN-based method using 3*3 kernels driven by a deeper architecture for high-grade glioma segmentation in MRI images is proposed in Reference 54.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

Alroobaea

Rubaiee

Bourouis

et al. 2019

Int J Imaging Syst Tech

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Biomedical image classification problem has attracted a lot of attention in medical engineering community and medicine applications. Accurate and automatic classification (eg, normal/abnormal or malignant/benign) has a variety of applications such as automatic decision making and is known to be very challenging. In this research, we address this problem by investigating the effectiveness of Bayesian inference methods for statistical bounded mixture models. Indeed, a novel approach termed as Bayesian learning for bounded generalized Gaussian mixture models is developed. The consideration of bounded mixture models is encouraged by their capability to take into account the nature of the data that is compactly supported. Furthermore, the consideration of Bayesian inference is more attractive compared to frequentist reasoning. In this work, we address main issues related to accurate data classification such as the effective estimation of the model's parameters and the selection of the optimal model complexity. Moreover, the problem of over-or under-fitting is treated by taking into account the uncertainty through introducing prior information about the model's parameters. A comparative study between different Gaussian-based models is also performed to evaluate the performance of the proposed framework. Experiments have been conducted on challenging biomedical image datasets that involve retinal images for diabetic retinopathy detection and mammograms for breast cancer detection. Obtained results are encouraging and show the benefits of our Bayesian framework. K E Y W O R D SBayesian inference, biomedical imaging, bounded mixture models, generalized Gaussian distribution, image classification, Markov chain Monte Carlo (MCMC)

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“…Content may change prior to final publication. already used in the speech and image recognition field [20]- [25]. Bayesian estimation can be divided into two categories: point estimation and predictive estimation.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Probabilistic Modeling Algorithm for the Aggregated Power Forecast Error of Multiple Newly Built Wind Farms

Jia

Shen

Wang

2019

IEEE Trans. Sustain. Energy

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The extensive penetration of wind farms (WFs) presents challenges to the operation of distribution networks (DNs). Building a probability distribution of the aggregated wind power forecast error is of great value for decision making. However, as a result of recent govern -ment incentives, many WFs are being newly built with little historical data for training distribution models. Moreover, WFs with different stakeholders may refuse to submit the raw data to a data center for model training.To address these problems, a Gaussian mixture model (GMM) is applied to build the distribution of the aggregated wind power forecast error; then, the maximum a posteriori (MAP) estimation method is adopted to overcome the limited training data problem in GMM parameter estimation. Next, a distributed MAP estimation method is developed based on the average consensus filter algorithm to address the data privacy issue. The distribution control center is introduced into the distributed estimation process to acquire more precise estimation results and better adapt to the DN control architecture. The effectiveness of the proposed algorithm is empirically verified using historical data. His research interests include power system probabilistic analysis and renewable energy generation.Chen Shen (M'98-SM'07) received his B.E. and Ph.D. degrees in Electrical

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Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images

Cited by 32 publications

References 24 publications

Enhanced IR face recognition using Bayesian unsupervised algorithm

Enhanced IR face recognition using Bayesian unsupervised algorithm

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

A Distributed Probabilistic Modeling Algorithm for the Aggregated Power Forecast Error of Multiple Newly Built Wind Farms

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