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Toward Predictive Multiscale Modeling of Vascular Tumor Growth

ARCHIVES OF COMPUTATIONAL methods IN ENGINEERING 2016年第4期23卷 735-779页

作者： Oden, J. Tinsley Lima, Ernesto A. B. F. Almeida, Regina C. Feng, Yusheng Rylander, Marissa Nichole Fuentes, David Faghihi, Danial Rahman, Mohammad M. DeWitt, Matthew Gadde, Manasa Zhou, J. Cliff Univ Texas Austin ICES 201 East 24th StPOB 4-102 Austin TX 78712 USA Natl Lab Sci Comp LNCC Ave Getulio Vargas 333 Petrpolis Brazil Univ Texas Austin Dept Mech Engn Austin TX 78712 USA Univ Texas San Antonio Ctr Simulat Visualizat & Real Time Predict SiViRT One UTSA CircleAET 2-332 San Antonio TX 78249 USA Univ Texas MD Anderson Canc Ctr Dept Imaging Phys 1515 Holcombe BlvdUnit 1902 Houston TX 77030 USA Virginia Tech Wake Forest Sch Biomed Engn & Sci 340 Kelly Hall Blacksburg VA 24061 USA Univ Texas Austin Dept Biomed Engn 107 W Dean KeetonBME Bldg Austin TX 78712 USA

New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patient-specific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the

关键词： Tumor growth models Bayesian calibration and validation 3D tumor platforms image processing

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Characterization of Phase Resolved Partial Discharge Waveforms from Instrument Transformer using statistical Signal processing Technique 4

Characterization of Phase Resolved Partial Discharge Wavefor...

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IEEE 2015 International Conference on Signal and image processing Applications (ICSIPA)

作者： Thayoob, Yasmin Hanum Md Ahmed, Syed Khaleel Piau, Chua Chun Ping, Chong Yu Balasubramaniam, Yogendra Univ Tenaga Nas Coll Engn Jalan IKRAM UNITEN Kajang 43000 Selangor Malaysia TNB Res Sdn Bhd Kajang 43000 Selangor Malaysia

ISBN: (纸本)9781479989966

Partial Discharge (PD) detection is one of the important methods used to evaluate the insulation condition of in-service high voltage (HV) electrical equipment. On-site PD detection methods are preferred compared to other methods as non-intrusive sensors are easier to install and have no interruptions on the operation. An electrical PD testing utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the in-service instrument transformer was used to detect partial discharge activities. However, the limitation of this method is the measurement often lacks of accuracy due to interference in the measured PD waveforms. Therefore, such PD signals are hard to be distinguished from the stochastic noise pulses. Hence, the characteristics of PD signals and interference signals are studied in the Phase Resolved Partial Discharge (PRPD) pattern in order to identify the extent of PD occurrence in the signals. The discharge magnitude could increase significantly because of the noise signals superposed with PD signals. Therefore a method that can remove high magnitude noise will be proposed by using statistical signal processing technique.

关键词： partial discharge instrument transformer inteferences phase resolved partial discharge pattern statistical signal processing technique

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Segmentation methods For Computer Aided Melanoma Detection

Segmentation Methods For Computer Aided Melanoma Detection

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Global Conference on Communication Technologies (GCCT)

作者： Santy, Adheena Joseph, Robin Mar Baselios Coll Engn & Technol Dept Comp Sci Trivandrum Kerala India

ISBN: (纸本)9781479985531

Melanoma is one of the deadly diseases among skin cancer. Melanoma detection can be done by dermatological screening and biopsy tests which are time consuming and expensive that requires experts from medical field. Due to cost of dermatologist to screen every patient, an automated system is needed for melanoma detection so that death rates can be minimized if detected early. It can be done using various image processing techniques. An important step in the automated system of melanoma detection is the segmentation process which locates the border of skin lesion in order to separate the lesion part from background skin for further feature extraction. This paper gives a study on various segmentation techniques that can be applied for melanoma detection using image processing. statistical region merging, iterative stochastic region merging, adaptive thresholding, color enhancement and iterative segmentation, multilevel thresholding are discussed in this paper. A comparative study of these segmentation methods is also performed based on the parameters accuracy, sensitivity and specificity. Multilevel thresholding has the highest accuracy and specificity and maximum sensitivity is obtained for iterative stochastic region merging.

关键词： melanoma skin cancer segmentation statistical region merging thresholding

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NON-PARAMETRIC ENSEMBLE KALMAN methods FOR THE INPAINTING OF NOISY DYNAMIC TEXTURES

NON-PARAMETRIC ENSEMBLE KALMAN METHODS FOR THE INPAINTING OF...

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IEEE International Conference on image processing (ICIP)

作者： Lguensat, Redouane Tandeo, Pierre Fablet, Ronan Ailliot, Pierre Inst Mines Telecom Brest France CNRS UMR 6285 Lab STICC Telecom Bretagne Brest France Univ Western Brittany CNRS UMR LMBA 6205 Brest France

ISBN: (纸本)9781479983391

In this work, we propose a novel non parametric method for the temporally-consistent inpainting of dynamic texture sequences. The inpainting of texture image sequences is stated as a stochastic assimilation issue, for which a novel model-free and data-driven Ensemble Kalman method is introduced. Our model is inspired by the Analog Ensemble Kalman Filter (AnEnKF) recently proposed for the assimilation of geophysical space-time dynamics, where the physical model is replaced by the use of statistical analogs or nearest neighbours. Such a non-parametric framework is of key interest for image processing applications, as prior models are seldom available in general. We present experimental evidence for real dynamic texture that using only a catalog database of historical data and without having any assumption on the model, the proposed method provides relevant dynamicallyconsistent interpolation and outperforms the classical parametric (autoregressive) dynamical prior.

关键词： Data assimilation Dynamic textures Inpainting Ensemble kalman filter Nearest-Neighbors Data mining

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Determining fluctuation in bio-nanomachines from electron microscopy images

Determining fluctuation in bio-nanomachines from electron mi...

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IEEE International Conference on image processing

作者： Yunye Gong Peter C. Doerschuk School of Electrical and Computer Engineering Cornell University Ithaca NY USA

Cryo electron microscopy leads to 3-D image reconstruction problems. The data is one projection of each of many different instances of the object. When the object is heterogeneous, it is natural to describe the object as stochastic with unknown statistics and seek to estimate the statistics from the image data. The result is not a unique reconstruction but rather a statistical description of an ensemble of reconstructions where the mean of the ensemble is a natural choice if a unique reconstruction is desired. This paper describes maximum likelihood methods for estimating the statistics via an expectation-maximization algorithm and multiple algorithmic optimizations to speed up the calculations.

关键词： image reconstruction Maximum likelihood estimation Scattering Fourier transforms Electron microscopy Software

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Segmentation methods for computer aided melanoma detection

Segmentation methods for computer aided melanoma detection

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Global Conference on Communication Technologies (GCCT)

作者： Adheena Santy Robin Joseph Dept. of Computer Science Mar Baselios College of Engineering & Technology Trivandrum India

关键词： Malignant tumors Merging image color analysis image segmentation Lesions Skin Feature extraction

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Characterization of Phase Resolved Partial Discharge Waveforms from Instrument Transformer using statistical Signal processing Technique

Characterization of Phase Resolved Partial Discharge Wavefor...

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IEEE International Conference on Signal and image processing Applications

作者： Yasmin Hanum Md Thayoob Syed Khaleel Ahmed Chua Chun Piau Chong Yu Ping Yogendra Balasubramaniam College of Engineering University Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Malaysia TNB Research Sdn. Bhd. Lorong Ayer Itam 43000 Kajang Selangor

ISBN: (纸本)9781479989973

关键词： partial discharge instrument transformer inteferences phase resolved partial discharge pattern statistical signal processing technique Electric instrument transformers Partial discharges Palladium Physically Handicapped Proportional derivative Professional Development Phase noise signal Shot noise

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NON-PARAMETRIC ENSEMBLE KALMAN methods FOR THE INPAINTING OF NOISY DYNAMIC TEXTURES

NON-PARAMETRIC ENSEMBLE KALMAN METHODS FOR THE INPAINTING OF...

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IEEE International Conference on image processing

作者： Redouane Lguensat Pierre Tandeo Ronan Fablet Pierre Ailliot Institut Mines-Telecom Telecom Bretagne CNRS UMR 6285 Lab-STICC University of Western Brittany CNRS UMR 6205 LMBA

ISBN: (纸本)9781479983407

In this work, we propose a novel non parametric method for the temporally-consistent inpainting of dynamic texture sequences. The inpainting of texture image sequences is stated as a stochastic assimilation issue, for which a novel model-free and data-driven Ensemble Kalman method is introduced. Our model is inspired by the Analog Ensemble Kalman Filter (AnEnKF) recently proposed for the assimilation of geophysical space-time dynamics, where the physical model is replaced by the use of statistical analogs or nearest neighbours. Such a non-parametric framework is of key interest for image processing applications, as prior models are seldom available in general. We present experimental evidence for real dynamic texture that using only a catalog database of historical data and without having any assumption on the model, the proposed method provides relevant dynamically-consistent interpolation and outperforms the classical parametric (autoregressive) dynamical prior.

关键词： Data assimilation Dynamic textures Inpainting Ensemble kalman filter Nearest-Neighbors Data mining

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Bayesian cluster identification in single-molecule localization microscopy data

Bayesian cluster identification in single-molecule localizat...

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作者： Patrick Rubin-Delanchy Garth L Burn Juliette Griffié David J Williamson Nicholas A Heard Andrew P Cope Dylan M Owen

Single-molecule localization-based super-resolution microscopy techniques such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) produce pointillist data sets of molecular coordinates. Although many algorithms exist for the identification and localization of molecules from raw image data, methods for analyzing the resulting point patterns for properties such as clustering have remained relatively under-studied. Here we present a model-based Bayesian approach to evaluate molecular cluster assignment proposals, generated in this study by analysis based on Ripley's K function. The method takes full account of the individual localization precisions calculated for each emitter. We validate the approach using simulated data, as well as experimental data on the clustering behavior of CD3ζ, a subunit of the CD3 T cell receptor complex, in resting and activated primary human T cells. less

关键词： Fluorescence Microscopy image processing Super-resolution Microscopy Single-molecule Localization Microscopy Photoactivated Localization Microscopy stochastic Optical Reconstruction Microscopy TIRF Microscopy statistical Calculation Single-molecule biophysics Anti-CD3 Anti-CD28

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MAXIMUM MARGINAL LIKELIHOOD ESTIMATION OF THE GRANULARITY COEFFICIENT OF A POTTS-MARKOV RANDOM FIELD WITHIN AN MCMC ALGORITHM

MAXIMUM MARGINAL LIKELIHOOD ESTIMATION OF THE GRANULARITY CO...

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IEEE Workshop on statistical Signal processing (SSP)

作者： Pereyra, Marcelo Whiteley, Nick Andrieu, Christophe Tourneret, Jean-Yves Univ Bristol Sch Math Bristol BS8 1TW Avon England Univ Toulouse IRIT INP ENSEEIHT TeSA F-31071 Toulouse France

ISBN: (纸本)9781479949755

This paper addresses the problem of estimating the Potts-Markov random field parameter beta jointly with the unknown parameters of a Bayesian image segmentation model. We propose a new adaptive Markov chain Monte Carlo (MCMC) algorithm for performing joint maximum marginal likelihood estimation of beta and maximum-a-posteriori unsupervised image segmentation. The method is based on a stochastic gradient adaptation technique whose computational complexity is significantly lower than that of the competing MCMC approaches. This adaptation technique can be easily integrated to existing MCMC methods where beta was previously assumed to be known. Experimental results on synthetic data and on a real 3D real image show that the proposed method produces segmentation results that are as good as those obtained with state-of-the-art MCMC methods and at much lower computational cost.

关键词： image segmentation Bayesian inference stochastic gradient Markov chain Monte Carlo Potts-Markov random field Intractable normalizing constants

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