Mixture Models for Image Analysis

Forbes, Florence

doi:10.1201/9780429055911-16

Cited by 4 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pixels in the image are first separated into two classes based on pixel intensity using a Poisson mixture model [Fig. 4(b)] (Forbes, 2018). Mixture model separation works because the distribution of pixels in the image can be accurately decomposed into low-intensity pixels coming from the thick grid bars in the surrounding background and higher-intensity pixels coming from the much thinner squares (Fig.…”

Section: Square Localizationmentioning

confidence: 99%

Learning to automate cryo-electron microscopy data collection with Ptolemy

Kim¹,

Noble²,

Cheng³

et al. 2023

IUCrJ

View full text Add to dashboard Cite

Over the past decade, cryo-electron microscopy (cryoEM) has emerged as an important method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software.

show abstract

Section: Square Localizationmentioning

confidence: 99%

Learning to automate cryo-electron microscopy data collection with Ptolemy

Kim¹,

Noble²,

Cheng³

et al. 2023

IUCrJ

View full text Add to dashboard Cite

show abstract

“…However, the massive growth in data acquisition and technologies has led to a number of interesting extensions. This includes combining multiple data sources through data integration [18][19][20], hierarchical Bayesian frameworks for partially exchangeable or nested data [21][22][23][24][25][26][27][28], hidden Markov models and other extensions for temporal data [29,30], accounting for spatially indexed data [31][32][33], incorporating general covariate information [34][35][36][37] and more.…”

Section: Bayesian Cluster Analysismentioning

confidence: 99%

Bayesian cluster analysis

Wade

2023

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering structure and patterns within each cluster. An overview of Bayesian cluster analysis is provided, including both model-based and loss-based approaches, along with a discussion on the importance of the kernel or loss selected and prior specification. Advantages are demonstrated in an application to cluster cells and discover latent cell types in single-cell RNA sequencing data to study embryonic cellular development. Lastly, we focus on the ongoing debate between finite and infinite mixtures in a model-based approach and robustness to model misspecification. While much of the debate and asymptotic theory focuses on the marginal posterior of the number of clusters, we empirically show that quite a different behaviour is obtained when estimating the full clustering structure. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.

show abstract

“…Gaussian mixing model: GMM suggested by many neuroimaging researchers [76,[168][169][170][171][172] is easy to implement. Effective and robust due to its probabilistic basis.…”

mentioning

confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

View full text Add to dashboard Cite

Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

show abstract

Mixture Models for Image Analysis

Cited by 4 publications

References 39 publications

Learning to automate cryo-electron microscopy data collection with Ptolemy

Learning to automate cryo-electron microscopy data collection with Ptolemy

Bayesian cluster analysis

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Contact Info

Product

Resources

About