1st Edition
Stochastic Modeling for Medical Image Analysis
Stochastic Modeling for Medical Image Analysis provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis.
Today, image-guided computer-assisted diagnostics (CAD) faces two basic challenging problems. The first is the computationally feasible and accurate modeling of images from different modalities to obtain clinically useful information. The second is the accurate and fast inferring of meaningful and clinically valid CAD decisions and/or predictions on the basis of model-guided image analysis.
To help address this, this book details original stochastic appearance and shape models with computationally feasible and efficient learning techniques for improving the performance of object detection, segmentation, alignment, and analysis in a number of important CAD applications.
The book demonstrates accurate descriptions of visual appearances and shapes of the goal objects and their background to help solve a number of important and challenging CAD problems. The models focus on the first-order marginals of pixel/voxel-wise signals and second- or higher-order Markov-Gibbs random fields of these signals and/or labels of regions supporting the goal objects in the lattice.
This valuable resource presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout.
Medical Imaging Modalities
Magnetic Resonance Imaging
Computed Tomography
Ultrasound Imaging
Nuclear Medical Imaging (Nuclide Imaging)
Bibliographic and Historical Notes
From Images to Graphical Models
Basics of Image Modeling
Pixel/Voxel Interactions and Neighborhoods
Exponential Families of Probability Distributions
Appearance and Shape Modeling
Bibliographic and Historical Notes
IRF Models: Estimating Marginals
Basic Independent Random Fields
Supervised and Unsupervised Learning
Expectation-Maximization to Identify Mixtures
Gaussian Linear Combinations versus Mixtures
Bibliographic and Historical Notes
Markov-Gibbs Random Field Models: Estimating Signal Interactions
Generic Kth-Order MGRFs
Common Second- and Higher-Order MGRFs
Learning Second-Order Interaction Structures
Bibliographic and Historical Notes
Applications: Image Alignment
General Image Alignment Frameworks
Global Alignment by Learning an Appearance Prior
Bibliographic and Historical Notes
Segmenting Multimodal Images
Joint MGRF of Images and Region Maps
Experimental Validation
Bibliographic and Historical Notes
Performance Evaluation and Validation
Segmenting with Deformable Models
Appearance-Based Segmentation
Shape and Appearance-Based Segmentation
Bibliographic and Historical Notes
Segmenting with Shape and Appearance Priors
Learning a Shape Prior
Evolving a Deformable Boundary
Experimental Validation
Bibliographic and Historical Notes
Cine Cardiac MRI Analysis
Segmenting Myocardial Borders
Wall Thickness Analysis
Experimental Results
Bibliographic and Historical Notes
Sizing Cardiac Pathologies
LV Wall Segmentation
Identifying the Pathological Tissue
Quantifying the Myocardial Viability
Performance Evaluation and Validation
Bibliographic and Historical Notes
Biography
Ayman El-Baz, PhD, associate professor, Department of Bioengineering, University of Louisville, Kentucky, USA
Georgy Gimel’farb, professor of computer science, University of Auckland, New Zealand
Jasjit S. Suri, PhD, MBA, CEO, Global Biomedical Technologies, Inc., Roseville, California, USA