Handbook of Biomedical Imaging : Methodologies and Clinical Research / edited by Nikos Paragios, James Duncan, Nicholas Ayache.

Format:

Book

Contributor:

Paragios, Nikos, editor.

Duncan, James, 1951- editor.

Ayache, Nicholas, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Optical data processing.

Radiology.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Imaging / Radiology.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Imaging / Radiology.

Physical Description:

1 online resource (VII, 511 pages) : 154 illustrations

Edition:

First edition 2015.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer US : Imprint: Springer, 2015.

System Details:

text file PDF

Summary:

Biomedical image analysis has become a major aspect of engineering sciences, and radiology in particular has become a dominant player in the field. Recent developments have made it possible to use biomedical imaging to view the human body from an anatomical or physiological perspective in a non-invasive fashion. Computer-aided diagnosis consists of developing algorithms and intelligent software components that can automatically process images and spot potential irregularities in the health chain. This book explains the process of computer assisted biomedical image analysis diagnosis through mathematical modeling and inference of image-based bio-markers. It covers five crucial thematic areas: methodologies, statistical and physiological models, biomedical perception, clinical biomarkers, and emerging modalities and domains. The dominant state-of-the-art methodologies for content extraction and interpretation of medical images include fuzzy methods, level set methods, kernel methods, and geometric deformable models. The models and techniques discussed are used in the diagnosis, planning, control and follow-up of medical procedures. Throughout the book, challenges and limitations are explored along with new research directions. This complete volume is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors. This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses.

Contents:

Object Segmentation and Markov Random Fields

Fuzzy methods in medical imaging

Curve Propagation, Level Set Methods and Grouping

Kernel Methods in Medical Imaging

Geometric Deformable Models: Overview and Recent Developments

Active Shape and Appearance Models

Statistical Atlases

Statistical Computing on Non-Linear Spaces for Computational Anatomy

Building Patient-Specific Physical and Physiological Computational Models from Medical Images

Constructing a Patient-Specific Model Heart from CT Data

Image-based haemodynamics simulation in intracranial aneursyms

Atlas-based Segmentation

Integration of Topological Constraints in Medical Image Segmentation

Monte Carlo Sampling for the Segmentation of Tubular Structures

Non-rigid registration using free-form deformations

Image registration using mutual information

Physical Model Based Recovery of Displacement and Deformations from 3D medical images

Cardiovascular Informatics

Rheumatoid Arthritis Quantifiction using Appearance Models

Medical Image Processing for Analysis of Colon Motility

Segmentation of Diseased Livers: A 3D Refinement Approach

Intra and inter subject analyses of brain functional Magnetic Resonance Images (fMRI)

Diffusion Tensor Estimation, Regularization and Classification

From Local Q-Ball Estimation to Fibre Crossing Tractography

Segmentation of clustered cells in microscopy images by geometric PDEs and level sets

Atlas-based whole-body registration in mice.

Other Format:

Printed edition:

ISBN:

978-0-387-09749-7

9780387097497

Access Restriction:

Restricted for use by site license.