This document summarizes two Bayesian approaches to medical image segmentation - k-means with posterior diffusion and hidden Markov random field models. It discusses potential extensions, such as using an external field to incorporate organ size and position variability, or a hybrid level set model. The conclusions discuss references for further information on these methods.
An improved Spread Spectrum Watermarking technique to withstand Geometric Def...IDES Editor
Here, we propose a new method for the
watermarking to withstand the geometric attacks, which
may occur during the transmission of the watermarked
image. The underlying system is based on Direct Sequence
Code Division Multiple Access (DS-CDMA). The algorithm
for the normalization has been formulated for use in black
and white images. The watermark is spread across the
carrier image by using the pseudo-random noise sequences
of optimal period and retrieval is made by the use of
correlation. Private Key technique is used so the
transmission is very secure. Matlab was used to implement
the algorithm discussed here.
An improved Spread Spectrum Watermarking technique to withstand Geometric Def...IDES Editor
Here, we propose a new method for the
watermarking to withstand the geometric attacks, which
may occur during the transmission of the watermarked
image. The underlying system is based on Direct Sequence
Code Division Multiple Access (DS-CDMA). The algorithm
for the normalization has been formulated for use in black
and white images. The watermark is spread across the
carrier image by using the pseudo-random noise sequences
of optimal period and retrieval is made by the use of
correlation. Private Key technique is used so the
transmission is very secure. Matlab was used to implement
the algorithm discussed here.
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
This paper presents a novel technique for
designing an Infinite Impulse Response (IIR) Filter with
Linear Phase Response. The design of IIR filter is always a
challenging task due to the reason that a Linear Phase
Response is not realizable in this kind. The conventional
techniques involve large number of samples and higher
order filter for better approximation resulting in complex
hardware for implementing the same. In addition, an
extensive computational resource for obtaining the inverse
of huge matrices is required. However, we propose a
technique, which uses the frequency domain sampling along
with the linear programming concept to achieve a filter
design, which gives a best approximation for the linear
phase response. The proposed method can give the closest
response with less number of samples (only 10) and is
computationally simple. We have presented the filter design
along with its formulation and solving methodology.
Numerical results are used to substantiate the efficiency of
the proposed method.
Unsupervised multispectral image Classification By fuzzy hidden Markov chains...CSCJournals
This paper deals with unsupervised classification of multi-spectral images, we propose to use a new vectorial fuzzy version of Hidden Markov Chains (HMC). The main characteristic of the proposed model is to allow the coexistence of crisp pixels (obtained with the uncertainty measure of the model) and fuzzy pixels (obtained with the fuzzy measure of the model) in the same image. Crisp and fuzzy multi-dimensional densities can then be estimated in the classification process, according to the assumption considered to model the statistical links between the layers of the multi-band image. The efficiency of the proposed method is illustrated with a Synthetic and real SPOTHRV images in the region of Rabat. The comparisons of two methods: fuzzy HMC and HMC are also provided. The classification results show the interest of the fuzzy HMC method.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MULTIFOCUS IMAGE FUSION USING MULTIRESOLUTION APPROACH WITH BILATERAL GRADIEN...cscpconf
The fusion of two or more images is required for images captured using different sensors,
different modalities or different camera settings to produce the image which is more suitable for
computer processing and human visual perception. The optical lenses in the cameras are having
limited depth of focus so it is not possible to acquire an image that contains all the objects infocus.
In this case we need a Multifocus image fusion technique to create a single image where
all objects are in-focus by combining relevant information in the two or more images. As the
sharp images contain more information than blurred images image sharpness will be taken as
one of the relevant information in framing the fusion rule. Many existing algorithms use
contrast or high local energy as a measure of local sharpness (relevant information). In
practice particularly in multimodal image fusion this assumption is not true. Here in this paper
we are proposing the method which combines the multiresolution transform and local phase
coherence measure to measure the sharpness in the images. The performance of the fusion
process was evaluated with mutual information, edge-association and spatial frequency as
quality metrics and compared with Laplacian pyramid, DWT (Discrete Wavelet Transform) and
bilateral gradient based sharpness criterion methods etc. The results showed that the proposed
algorithm is performing better than the existing ones.
Despeckling of Ultrasound Imaging using Median Regularized Coupled PdeIDES Editor
This paper presents an approach for reducing speckle
in ultrasound images using Coupled Partial Differential
Equation (CPDE) which has been obtained by uniting secondorder
and the fourth-order partial differential equations. Using
PDE to reduce the speckle is the noise-smoothing methods
which is getting attention widely, because PDE can keep the
edge well when it reduces the noise. We also introduced a
median regulator to guide energy source to boost the features
in the image and regularize the diffusion. The proposed
method is tested in both simulated and real medical
ultrasound images. The proposed method is compared with
SRAD, Perona Malik diffusion and Non linear coherent
diffusion methods, our method gives better result in terms of
CNR, SSIM and FOM.
Conjugate Gradient method for Brain Magnetic Resonance Images SegmentationEL-Hachemi Guerrout
Image segmentation is the process of partitioning the im-
age into regions of interest in order to provide a meaningful represen-
tation of information. Nowadays, segmentation has become a necessity
in many practical medical imaging methods as locating tumors and dis-
eases. Hidden Markov Random Field model is one of several techniques
used in image segmentation. It provides an elegant way to model the
segmentation process. This modeling leads to the minimization of an ob-
jective function. Conjugate Gradient algorithm (CG) is one of the best
known optimization techniques. This paper proposes the use of the non-
linear Conjugate Gradient algorithm (CG) for image segmentation, in
combination with the Hidden Markov Random Field modelization. Since
derivatives are not available for this expression, finite differences are used
in the CG algorithm to approximate the first derivative. The approach
is evaluated using a number of publicly available images, where ground
truth is known. The Dice Coefficient is used as an objective criterion to
measure the quality of segmentation. The results show that the proposed
CG approach compares favorably with other variants of Hidden Markov
Random Field segmentation algorithms.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
An Optimized Transform for ECG Signal CompressionIDES Editor
A significant feature of the coming digital era is the
exponential increase in digital data, obtained from various
signals specially the biomedical signals such as
electrocardiogram (ECG), electroencephalogram (EEG),
electromyogram (EMG) etc. How to transmit or store these
signals efficiently becomes the most important issue. A digital
compression technique is often used to solve this problem.
This paper proposed a comparative study of transform based
approach for ECG signal compression. Adaptive threshold is
used on the transformed coefficients. The algorithm is tested
for 10 different records from MIT-BIH arrhythmia database
and obtained percentage root mean difference as around
0.528 to 0.584% for compression ratio of 18.963:1 to 23.011:1
for DWT. Among DFT, DCT and DWT techniques, DWT has
been proven to be very efficient for ECG signal coding.
Further improvement in the CR is possible by efficient
entropy coding.
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
This paper presents a novel technique for
designing an Infinite Impulse Response (IIR) Filter with
Linear Phase Response. The design of IIR filter is always a
challenging task due to the reason that a Linear Phase
Response is not realizable in this kind. The conventional
techniques involve large number of samples and higher
order filter for better approximation resulting in complex
hardware for implementing the same. In addition, an
extensive computational resource for obtaining the inverse
of huge matrices is required. However, we propose a
technique, which uses the frequency domain sampling along
with the linear programming concept to achieve a filter
design, which gives a best approximation for the linear
phase response. The proposed method can give the closest
response with less number of samples (only 10) and is
computationally simple. We have presented the filter design
along with its formulation and solving methodology.
Numerical results are used to substantiate the efficiency of
the proposed method.
Unsupervised multispectral image Classification By fuzzy hidden Markov chains...CSCJournals
This paper deals with unsupervised classification of multi-spectral images, we propose to use a new vectorial fuzzy version of Hidden Markov Chains (HMC). The main characteristic of the proposed model is to allow the coexistence of crisp pixels (obtained with the uncertainty measure of the model) and fuzzy pixels (obtained with the fuzzy measure of the model) in the same image. Crisp and fuzzy multi-dimensional densities can then be estimated in the classification process, according to the assumption considered to model the statistical links between the layers of the multi-band image. The efficiency of the proposed method is illustrated with a Synthetic and real SPOTHRV images in the region of Rabat. The comparisons of two methods: fuzzy HMC and HMC are also provided. The classification results show the interest of the fuzzy HMC method.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MULTIFOCUS IMAGE FUSION USING MULTIRESOLUTION APPROACH WITH BILATERAL GRADIEN...cscpconf
The fusion of two or more images is required for images captured using different sensors,
different modalities or different camera settings to produce the image which is more suitable for
computer processing and human visual perception. The optical lenses in the cameras are having
limited depth of focus so it is not possible to acquire an image that contains all the objects infocus.
In this case we need a Multifocus image fusion technique to create a single image where
all objects are in-focus by combining relevant information in the two or more images. As the
sharp images contain more information than blurred images image sharpness will be taken as
one of the relevant information in framing the fusion rule. Many existing algorithms use
contrast or high local energy as a measure of local sharpness (relevant information). In
practice particularly in multimodal image fusion this assumption is not true. Here in this paper
we are proposing the method which combines the multiresolution transform and local phase
coherence measure to measure the sharpness in the images. The performance of the fusion
process was evaluated with mutual information, edge-association and spatial frequency as
quality metrics and compared with Laplacian pyramid, DWT (Discrete Wavelet Transform) and
bilateral gradient based sharpness criterion methods etc. The results showed that the proposed
algorithm is performing better than the existing ones.
Despeckling of Ultrasound Imaging using Median Regularized Coupled PdeIDES Editor
This paper presents an approach for reducing speckle
in ultrasound images using Coupled Partial Differential
Equation (CPDE) which has been obtained by uniting secondorder
and the fourth-order partial differential equations. Using
PDE to reduce the speckle is the noise-smoothing methods
which is getting attention widely, because PDE can keep the
edge well when it reduces the noise. We also introduced a
median regulator to guide energy source to boost the features
in the image and regularize the diffusion. The proposed
method is tested in both simulated and real medical
ultrasound images. The proposed method is compared with
SRAD, Perona Malik diffusion and Non linear coherent
diffusion methods, our method gives better result in terms of
CNR, SSIM and FOM.
Conjugate Gradient method for Brain Magnetic Resonance Images SegmentationEL-Hachemi Guerrout
Image segmentation is the process of partitioning the im-
age into regions of interest in order to provide a meaningful represen-
tation of information. Nowadays, segmentation has become a necessity
in many practical medical imaging methods as locating tumors and dis-
eases. Hidden Markov Random Field model is one of several techniques
used in image segmentation. It provides an elegant way to model the
segmentation process. This modeling leads to the minimization of an ob-
jective function. Conjugate Gradient algorithm (CG) is one of the best
known optimization techniques. This paper proposes the use of the non-
linear Conjugate Gradient algorithm (CG) for image segmentation, in
combination with the Hidden Markov Random Field modelization. Since
derivatives are not available for this expression, finite differences are used
in the CG algorithm to approximate the first derivative. The approach
is evaluated using a number of publicly available images, where ground
truth is known. The Dice Coefficient is used as an objective criterion to
measure the quality of segmentation. The results show that the proposed
CG approach compares favorably with other variants of Hidden Markov
Random Field segmentation algorithms.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
An Optimized Transform for ECG Signal CompressionIDES Editor
A significant feature of the coming digital era is the
exponential increase in digital data, obtained from various
signals specially the biomedical signals such as
electrocardiogram (ECG), electroencephalogram (EEG),
electromyogram (EMG) etc. How to transmit or store these
signals efficiently becomes the most important issue. A digital
compression technique is often used to solve this problem.
This paper proposed a comparative study of transform based
approach for ECG signal compression. Adaptive threshold is
used on the transformed coefficients. The algorithm is tested
for 10 different records from MIT-BIH arrhythmia database
and obtained percentage root mean difference as around
0.528 to 0.584% for compression ratio of 18.963:1 to 23.011:1
for DWT. Among DFT, DCT and DWT techniques, DWT has
been proven to be very efficient for ECG signal coding.
Further improvement in the CR is possible by efficient
entropy coding.
The main machine learning algorithms are built upon various mathematical foundations such as statistics, optimization, and probability. Will this also hold true for Artificial Intelligence? In this presentation, I will showcase some recent examples of interactions between machine learning and mathematics.
Colloquium @ CEREMADE (October 3, 2023)
EXPERT SYSTEMS AND SOLUTIONS
Project Center For Research in Power Electronics and Power Systems
IEEE 2010 , IEEE 2011 BASED PROJECTS FOR FINAL YEAR STUDENTS OF B.E
Email: expertsyssol@gmail.com,
Cell: +919952749533, +918608603634
www.researchprojects.info
OMR, CHENNAI
IEEE based Projects For
Final year students of B.E in
EEE, ECE, EIE,CSE
M.E (Power Systems)
M.E (Applied Electronics)
M.E (Power Electronics)
Ph.D Electrical and Electronics.
Training
Students can assemble their hardware in our Research labs. Experts will be guiding the projects.
EXPERT GUIDANCE IN POWER SYSTEMS POWER ELECTRONICS
We provide guidance and codes for the for the following power systems areas.
1. Deregulated Systems,
2. Wind power Generation and Grid connection
3. Unit commitment
4. Economic Dispatch using AI methods
5. Voltage stability
6. FLC Control
7. Transformer Fault Identifications
8. SCADA - Power system Automation
we provide guidance and codes for the for the following power Electronics areas.
1. Three phase inverter and converters
2. Buck Boost Converter
3. Matrix Converter
4. Inverter and converter topologies
5. Fuzzy based control of Electric Drives.
6. Optimal design of Electrical Machines
7. BLDC and SR motor Drives
This 10 hours class is intended to give students the basis to empirically solve statistical problems. Talk 1 serves as an introduction to the statistical software R, and presents how to calculate basic measures such as mean, variance, correlation and gini index. Talk 2 shows how the central limit theorem and the law of the large numbers work empirically. Talk 3 presents the point estimate, the confidence interval and the hypothesis test for the most important parameters. Talk 4 introduces to the linear regression model and Talk 5 to the bootstrap world. Talk 5 also presents an easy example of a markov chains.
All the talks are supported by script codes, in R language.
bayesImageS: an R package for Bayesian image analysisMatt Moores
There are many approaches to Bayesian computation with intractable likelihoods, including the exchange algorithm, approximate Bayesian computation (ABC), thermodynamic integration, and composite likelihood. These approaches vary in accuracy as well as scalability for datasets of significant size. The Potts model is an example where such methods are required, due to its intractable normalising constant. This model is a type of Markov random field, which is commonly used for image segmentation. The dimension of its parameter space increases linearly with the number of pixels in the image, making this a challenging application for scalable Bayesian computation. My talk will introduce various algorithms in the context of the Potts model and describe their implementation in C++, using OpenMP for parallelism. I will also discuss the process of releasing this software as an open source R package on the CRAN repository.
Bayesian Inference and Uncertainty Quantification for Inverse ProblemsMatt Moores
So-called “inverse” problems arise when the parameters of a physical system cannot be directly observed. The mapping between these latent parameters and the space of noisy observations is represented as a mathematical model, often involving a system of differential equations. We seek to infer the parameter values that best fit our observed data. However, it is also vital to obtain accurate quantification of the uncertainty involved with these parameters, particularly when the output of the model will be used for forecasting. Bayesian inference provides well-calibrated uncertainty estimates, represented by the posterior distribution over the parameters. In this talk, I will give a brief introduction to Markov chain Monte Carlo (MCMC) algorithms for sampling from the posterior distribution and describe how they can be combined with numerical solvers for the forward model. We apply these methods to two examples of ODE models: growth curves in ecology, and thermogravimetric analysis (TGA) in chemistry. This is joint work with Matthew Berry, Mark Nelson, Brian Monaghan and Raymond Longbottom.
R package bayesImageS: Scalable Inference for Intractable LikelihoodsMatt Moores
There are many approaches to Bayesian computation with intractable likelihoods, including the exchange algorithm and approximate Bayesian computation (ABC). A serious drawback of these algorithms is that they do not scale well for models with a large state space. Markov random fields, such as the Ising/Potts model and exponential random graph model (ERGM), are particularly challenging because the number of discrete variables increases linearly with the size of the image or graph. The likelihood of these models cannot be computed directly, due to the presence of an intractable normalising constant. In this context, it is necessary to employ algorithms that provide a suitable compromise between accuracy and computational cost.
Bayesian indirect likelihood (BIL) is a class of methods that approximate the likelihood function using a surrogate model. This model can be trained using a pre-computation step, utilising massively parallel hardware to simulate auxiliary variables. We review various types of surrogate model that can be used in BIL. In the case of the Potts model, we introduce a parametric approximation to the score function that incorporates its known properties, such as heteroskedasticity and critical temperature. We demonstrate this method on 2D satellite remote sensing and 3D computed tomography (CT) images. We achieve a hundredfold improvement in the elapsed runtime, compared to the exchange algorithm or ABC. Our algorithm has been implemented in the R package “bayesImageS,” which is available from CRAN.
bayesImageS: Bayesian computation for medical Image Segmentation using a hidd...Matt Moores
There are many approaches to Bayesian computation with intractable likelihoods, including the exchange algorithm, approximate Bayesian computation (ABC), thermodynamic integration, and composite likelihood. These approaches vary in accuracy as well as scalability for datasets of significant size. The Potts model is an example where such methods are required, due to its intractable normalising constant. This model is a type of Markov random field, which is commonly used for image segmentation. The dimension of its parameter space increases linearly with the number of pixels in the image, making this a challenging application for scalable Bayesian computation. My talk will introduce various algorithms in the context of the Potts model and describe their implementation in C++, using OpenMP for parallelism.
Approximate Bayesian computation for the Ising/Potts modelMatt Moores
Bayes’ formula involves the likelihood function, p(y|theta), which is a problem when the likelihood is unavailable in closed form. ABC is a method for approximating the posterior p(theta|y) without evaluating the likelihood. Instead, pseudo-data is simulated from a generative model and compared with the observations. This talk will give an introduction to ABC algorithms: rejection sampling, ABC-MCMC and ABC-SMC. Application of these algorithms to image analysis will be presented as an illustrative example. These methods have been implemented in the R package bayesImageS.
This is joint work with Christian Robert (Warwick/Dauphine), Kerrie Mengersen and Christopher Drovandi (QUT).
Accelerating Pseudo-Marginal MCMC using Gaussian ProcessesMatt Moores
The grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms are pseudo-marginal methods used to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but less theoretical support. In this paper we accelerate the GIMH method by using a Gaussian process (GP) approximation to the log-likelihood and train this GP using a short pilot run of the MCWM algorithm. Our new method, GP-GIMH, is illustrated on simulated data from a stochastic volatility and a gene network model. Our approach produces reasonable estimates of the univariate and bivariate posterior distributions, and the posterior correlation matrix in these examples with at least an order of magnitude improvement in computing time.
R package 'bayesImageS': a case study in Bayesian computation using Rcpp and ...Matt Moores
There are many approaches to Bayesian computation with intractable likelihoods, including the exchange algorithm, approximate Bayesian computation (ABC), thermodynamic integration, and composite likelihood. These approaches vary in accuracy as well as scalability for datasets of significant size. The Potts model is an example where such methods are required, due to its intractable normalising constant. This model is a type of Markov random field, which is commonly used for image segmentation. The dimension of its parameter space increases linearly with the number of pixels in the image, making this a challenging application for scalable Bayesian computation. My talk will introduce various algorithms in the context of the Potts model and describe their implementation in C++, using OpenMP for parallelism. I will also discuss the process of releasing this software as an open source R package on the CRAN repository.
Bayesian modelling and computation for Raman spectroscopyMatt Moores
Raman spectroscopy can be used to identify molecules by the characteristic scattering of light from a laser. Each Raman-active dye label has a unique spectral signature, comprised by the locations and amplitudes of the peaks. The Raman spectrum is discretised into a multivariate observation that is highly collinear, hence it lends itself to a reduced-rank representation. We introduce a sequential Monte Carlo (SMC) algorithm to separate this signal into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. By incorporating this representation into a Bayesian functional regression, we can quantify the relationship between dye concentration and peak intensity. We also estimate the model evidence using SMC to investigate long-range dependence between peaks. These methods have been implemented as an R package, using RcppEigen and OpenMP.
Pre-computation for ABC in image analysisMatt Moores
MCMSki IV (the 5th IMS-ISBA joint meeting)
January 2014
Chamonix Mont-Blanc, France
The associated journal article has now been uploaded to arXiv: http://arxiv.org/abs/1403.4359
TEST BANK For Wong’s Essentials of Pediatric Nursing, 11th Edition by Marilyn...kevinkariuki227
TEST BANK For Wong’s Essentials of Pediatric Nursing, 11th Edition by Marilyn Hockenberry, Cheryl Rodgers, Verified Chapters 1 - 31, Complete Newest Version.pdf
TEST BANK For Wong’s Essentials of Pediatric Nursing, 11th Edition by Marilyn Hockenberry, Cheryl Rodgers, Verified Chapters 1 - 31, Complete Newest Version.pdf
TEST BANK For Advanced Practice Nursing in the Care of Older Adults, 2nd Edit...kevinkariuki227
TEST BANK For Advanced Practice Nursing in the Care of Older Adults, 2nd Edition by Laurie Kennedy-Malone, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK For Advanced Practice Nursing in the Care of Older Adults, 2nd Edition by Laurie Kennedy-Malone, Verified Chapters 1 - 19, Complete Newest Version.pdf
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
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Why invest into infodemic management in health emergenciesTina Purnat
A lecture discussing the challenge of health misinformation and information ecosystem in public health, how this impacts demand promotion in health, and how this then relates to responding to misinformation and infodemics in health emergencies. Appended with lots of tools, guidance and resources for people who want to do more reading.
The POPPY STUDY (Preconception to post-partum cardiovascular function in prim...
Informative Priors for Segmentation of Medical Images
1. Motivation Method 1 Method 2 Extensions Conclusion
Informative Priors for Segmentation
of Medical Images
Matt Moores1,2 , Cathy Hargrave3 , Fiona Harden2
& Kerrie Mengersen1
1 Discipline of Mathematical Sciences, Queensland University of Technology
2 Discipline of Medical Radiation Sciences, Queensland University of Technology
3 Radiation Oncology Mater Centre, Queensland Health
Bayes on the Beach, 2011
4. Motivation Method 1 Method 2 Extensions Conclusion
Distribution of Pixel Intensity
15000
15000
10000
10000
Frequency
Frequency
5000
5000
0
−1000 −800 −600 −400 −200 0 200 0 −1000 −800 −600 −400 −200 0 200
Hounsfield unit pixel intensity
(a) Fan-Beam CT (b) Cone-Beam CT
5. Motivation Method 1 Method 2 Extensions Conclusion
itkBayesianClassifierImageFilter
1 estimate µ using k-means
2 estimate σ 2 for each cluster
(mixing proportions are assumed equal)
3 create a matrix y∗ :
for each pixel yi and each cluster Ck ∼ N(µk , σk ),
yik = p(yi |µk , σk )
6
5
4 classify each pixel yi according to the largest value of yik
6. Motivation Method 1 Method 2 Extensions Conclusion
itkBayesianClassifierImageFilter
1 estimate µ using k-means
1 select initial values for µ
2 assign each pixel y to the nearest µk
3 recalculate each µk by averaging over the members of k
4 repeat steps 2 & 3 until none of the pixel assignments change
2 estimate σ 2 for each cluster
(mixing proportions are assumed equal)
3 create a matrix y∗ :
for each pixel yi and each cluster Ck ∼ N(µk , σk ),
yik = p(yi |µk , σk )
6
5
4 classify each pixel yi according to the largest value of yik
8. Motivation Method 1 Method 2 Extensions Conclusion
Prior
4 matrix pik representing the prior probability of pixel i
belonging to cluster k
then pixel classification is based on the posterior pik × yik
but:
this has no effect on the number of clusters, nor on their
parameters µk and σk
can’t use the posterior from one classification as the prior for
another, unless the clusters are the same
11. Motivation Method 1 Method 2 Extensions Conclusion
hidden Markov random field
Joint distribution of observed intensities y and unobserved labels z:
p(y, z|µ, τ ) ∝ p(y|µ, τ , z)p(z) (1)
1
yi |µj , τj , zi = j ∼ N µj , (2)
τj
N
p(z) = C(β)−1 exp αi (zi ) + β wij f (zi , zj ) (3)
i=1 i∼j
simple Potts model (without external field):
p(z) = C(β)−1 exp β I(zi = zj ) (4)
i∼j
12. Motivation Method 1 Method 2 Extensions Conclusion
informative prior for µ and τ
200
200
0
0
−200
−200
Hounsfield unit
pixel intensity
−400
−400
−600
−600
−800
−800
−1000
−1000
0 1 2 3 4 0 1 2 3 4
Electron Density Electron Density
(a) Fan-Beam CT (b) Cone-Beam CT
13. Motivation Method 1 Method 2 Extensions Conclusion
external field
N
In equation (3) earlier, the term exp i=1 αi (zi ) defines an
external field.
Figure: manual contours of the organs of interest.
14. Motivation Method 1 Method 2 Extensions Conclusion
external field II
Prior probabilities αi (zi ) for each pixel can be generated by
simulation, based on:
geometry of each organ, from the treatment plan
variability in size and position, from published studies
Axis prostate seminal vesicles
Ant-Post x = 0.1, sd = 4.1 mm x = 1.2, sd = 7.3 mm
Sup-Inf x = −0.5, sd = 2.9 mm x = −0.7, sd = 4.5 mm
Left-Right x = 0.2, sd = 0.9 mm x = −0.9, sd = 1.9 mm
Table: Mean x and standard deviation sd of observed [5] variability in
position, along three axes: anteroposterior (Ant-Post); superoinferior
(Sup-Inf); & lateral (Left-Right) relative to the patient.
16. Motivation Method 1 Method 2 Extensions Conclusion
hybrid model
Chen & Metaxas [6, 7] define the object boundary implicitly as the
zero level set of a cost function:
∂φi φi φi
= λ1 M i + λ 2 Pi · − (λ2 Pi + λ3 ) ·
∂t φi φi
(5)
where:
Mi is the inflation force (total gradient magnitude)
Pi is the local image force at each pixel
(probability of pixel j belonging to object i)
non-overlapping constraint
φi
· φi is the local curvature
(surface smoothness constraint)
17. Motivation Method 1 Method 2 Extensions Conclusion
Summary
Two Bayesian approaches to medical image segmentation:
k-means with posterior diffusion
(itkBayesianClassifierImageFilter)
hidden Markov random field
(PyMCMC)
Potential extensions to Potts MRF:
external field defined by size and position of objects
hybrid Level Set model
18. Motivation Method 1 Method 2 Extensions Conclusion
References I
P. Teo, G. Sapiro and B. Wandell (1997) Creating connected
representations of cortical gray matter for functional MRI
visualization. IEEE Trans. Med. Imag. 16: 852-863.
J. Melonakos, K. Krishnan and A. Tannenbaum (2006)
An ITK Filter for Bayesian Segmentation:
itkBayesianClassifierImageFilter The Insight Journal
http://hdl.handle.net/1926/160
Strickland, C. M., Denham, R. J., Alston, C. L., & Mengersen, K. L.
(2011) PyMCMC : a Python package for Bayesian Estimation using
Markov chain Monte Carlo. Journal of Statistical Software (In Press)
C. Alston, K. Mengersen, C. Robert, J. Thompson, P. Littlefield, D.
Perry and A. Ball (2007) Bayesian mixture models in a longitudinal
setting for analysing sheep CAT scan images. Computational
Statistics & Data Analysis 51(9): 4282-4296.
19. Motivation Method 1 Method 2 Extensions Conclusion
References II
S.J. Frank, L. Dong, R. J. Kudchadker, R. De Crevoisier, A. K. Lee,
R. Cheung, S. Choi, J. O’Daniel, S. L. Tucker, H. Wang, et al.
(2008) Quantification of Prostate and Seminal Vesicle Interfraction
Variation During IMRT. International Journal of Radiation
Oncology*Biology*Physics 71(3): 813-820.
T. Chen and D. Metaxas (2005) A hybrid framework for 3D medical
image segmentation. Medical Image Analysis 9(6): 547-565.
T. Chen, S. Kim, J. Zhou, D. Metaxas, G. Rajagopal & N. Yue
(2009) 3D Meshless Prostate Segmentation and Registration in
Image Guided Radiotherapy. In Proceedings of MICCAI 43-50.
P. Th´venaz, T. Blu & M. Unser (2000) Interpolation Revisited.
e
IEEE Trans. Medical Imaging 19(7): 739–758.
20. Motivation Method 1 Method 2 Extensions Conclusion
Acknowledgements
Bayesian Research & Applications Group at QUT
Radiation Oncology Mater Centre:
Emmanuel Baveas
Rebecca Owen
Timothy Deegan
Steven Sylvander
John Baines
Dr. Michael Poulsen