Bayesian and grAphical Models for Biomedical Imaging: First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers

✍ Scribed by M. Jorge Cardoso, Ivor Simpson, Tal Arbel, Doina Precup, Annemie Ribbens (eds.)

• Publisher: Springer International Publishing
• Year: 2014
• Tongue: English
• Leaves: 139
• Series: Lecture Notes in Computer Science 8677 Theoretical Computer Science and General Issues
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages -
N3 Bias Field Correction Explained as a Bayesian Modeling Method....Pages 1-12
A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging....Pages 13-24
Optimal Joint Segmentation and Tracking of Escherichia Coli in the Mother Machine....Pages 25-36
Physiologically Informed Bayesian Analysis of ASL fMRI Data....Pages 37-48
Bone Reposition Planning for Corrective Surgery Using Statistical Shape Model: Assessment of Differential Geometrical Features....Pages 49-60
An Inference Language for Imaging....Pages 61-72
An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation....Pages 73-84
Learning Imaging Biomarker Trajectories from Noisy Alzheimer’s Disease Data Using a Bayesian Multilevel Model....Pages 85-94
Four Neuroimaging Questions that P-Values Cannot Answer (and Bayesian Analysis Can)....Pages 95-106
Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies....Pages 107-117
A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions....Pages 118-129
Back Matter....Pages -

✦ Subjects

Algorithm Analysis and Problem Complexity; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition; Computer Graphics; Discrete Mathematics in Computer Science