Title Bayesian and grAphical models for biomedical imaging : first International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers / M. Jorge Cardoso, Ivor Simpson, Tal Arbel, Doina Precup, Annemie Ribbens (eds.)

Published Cham : Springer, 2014

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Description 1 online resource (x, 131 pages) : illustrations Series Lecture Notes in Computer Science, 0302-9743 ; 8677 LNCS sublibrary. SL 1, Theoretical computer science and general issues Lecture notes in computer science ; 8677. 0302-9743 LNCS sublibrary. SL 1, Theoretical computer science and general issues. Contents Intro; Preface; Organization; Table of Contents; N3 Bias Field Correction Explained as a Bayesian Modeling Method; 1 Introduction; 2 Methods; 2.1 The N3 Method in Its Practical Implementation; 2.2 EM-Based Bias Field Estimation; 2.3 N3 as an Approximate MAP Parameter Estimator; 3 Experiments; 4 Results; 5 Discussion; References; A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging; 1 Introduction; 2 Methods; 2.1 Multi-tensor Model with a Fixed Tensor Basis; 2.2 Mixture Fraction Estimation with Prior Knowledge; 3 Experiments 3.1 Digital Phantom3.2 In Vivo Tongue Diffusion Data; 4 Discussion; 5 Conclusion; References; Segmentation and Tracking of E. coli; 1 Introduction; 2 Microscopic Setup and Data Preprocessing; 3 Segmentation Methods; 3.1 Thresholding and Component Trees (CT); 3.2 Parametric Max-Flow (PMF); 3.3 Parametric Max-Flow and Random Forest (PMFRF); 4 A Graphical Model for Segmentation and Tracking; 4.1 Costs; 4.2 Constraints; 4.3 Eliminating Segmentation Variables; 4.4 Finding The Globally Optimal Solution; 5 Results; 6 Summary and Discussion; References Physiologically Informed Bayesian Analysis of ASL fMRI Data1 Introduction; 2 A Physiologically Informed ASL/BOLD Link; 2.1 The Extended Balloon Model; 2.2 Physiological Linear Relationship between Response Functions; 3 Bayesian Hierarchical Model for ASL Data Analysis; 4 A Physiologically Informed 2-steps Inference Procedure; 4.1 Hemodynamics Estimation Step M1; 4.2 Perfusion Response Estimation Step M2; 5 Simulation Results; 6 Real Data Results; 7 Discussion and Conclusion; References Bone Reposition Planning for Corrective Surgery Using Statistical Shape Model: Assessment of Differential Geometrical Features1 Introduction; 2 SSM Based Planning; 2.1 Fitting of the SSM to Two Bone Segements; 2.2 Probability Distribution Functions for Shape Validity and Scaling; 2.3 Probability Distribution Model to Compare Shapes; 3 Experiments; 3.1 Data; 3.2 Evaluating the SSM; 3.3 Evaluating Modes of Variation; 3.4 Including the Geometrical Features; 3.5 Accuracy of Bone Repositioning; 4 Discussion; 5 Appendix; An Inference Language for Imaging; 1 Introduction; 2 Methods 2.1 The Modeling Language2.2 The Inference Engine; 3 Motion-aware Positron Emission Tomography; 4 Conclusion; 5 Download; References; An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation; 1 Introduction; 2 Methods; 2.1 Data Cost Calculation Using Pharmacokinetic Model Prediction; 2.2 Optimization on the Reduced 4D Graph; 3 Results; 3.1 Algorithm Evaluation on Synthetic Data; 3.2 Pharmacokinetic Modelling and Motion Correction on DCE-MRI Images of Rectal Cancer; 4 Discussion and Conclusion; 5 Future Work; References Summary This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014. The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data Analysis computerwetenschappen computer sciences wiskunde mathematics beeldverwerking image processing machine vision patroonherkenning pattern recognition algoritmen algorithms computeranalyse computer analysis kunstmatige intelligentie artificial intelligence computergrafie computer graphics Information and Communication Technology (General) Informatie- en communicatietechnologie (algemeen) Notes Includes author index Online resource; title from PDF title page (SpringerLink, viewed October 10, 2014) Subject Image analysis -- Congresses Bioinformatics -- Congresses Bioinformatics Image analysis Engineering & Applied Sciences. Computer Science. Genre/Form Conference papers and proceedings Form Electronic book Author Cardoso, M. Jorge, editor. Simpson, Ivor, editor of compilation Arbel, Tal, editor of compilation. Precup, Doina, editor of compilation Ribbens, Annemie, editor of compilation ISBN 9783319122892 3319122894 3319122886 9783319122885

Other Titles BAMBI 2014

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