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 ImagingComputed Tomography Ultrasound Imaging Nuclear Medical Imaging (Nuclide Imaging) Bibliographic and Historical Notes From Images to Graphical Models Basics of Image ModelingPixel/Voxel Interactions and NeighborhoodsExponential Families of Probability DistributionsAppearance and Shape Modeling Bibliographic and Historical NotesIRF Models: Estimating Marginals Basic Independent Random Fields Supervised and Unsupervised LearningExpectation-Maximization to Identify Mixtures Gaussian Linear Combinations versus MixturesBibliographic and Historical NotesMarkov-Gibbs Random Field Models: Estimating Signal Interactions Generic Kth-Order MGRFsCommon 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 NotesSegmenting Multimodal Images Joint MGRF of Images and Region MapsExperimental ValidationBibliographic and Historical Notes Performance Evaluation and ValidationSegmenting with Deformable Models Appearance-Based SegmentationShape and Appearance-Based SegmentationBibliographic and Historical NotesSegmenting with Shape and Appearance Priors Learning a Shape Prior Evolving a Deformable BoundaryExperimental Validation Bibliographic and Historical NotesCine Cardiac MRI Analysis Segmenting Myocardial Borders Wall Thickness Analysis Experimental Results Bibliographic and Historical NotesSizing Cardiac Pathologies LV Wall Segmentation Identifying the Pathological Tissue Quantifying the Myocardial Viability Performance Evaluation and ValidationBibliographic and Historical Notes

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