Medical Image Analysis
1st Edition
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Description
Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC.
Key Features
- Provides an authoritative description of key concepts and methods
- Includes tutorial-based sections that clearly explain principles and their application to different medical domains
- Presents a representative selection of topics to match a modern and relevant approach to medical image computing
Readership
Researchers in Medical Image computing and analysis; Ideal Text-Ref for courses on medical image analysis in BEng/MEng/MSc programs in Biomedical Engineering, Medical Physics, Medical Engineering, or Computational Bioengineering
Table of Contents
Chapter 1 Historical perspective on Medical Image Analysis
Chapter 2 Brief Introduction to Medical Imaging Modalities
- Comparative synthesis of each technique
- Choosing it where is possible, living with it when not
Chapter 3 Mathematical Preliminaries and Notation
Image Representation
Chapter 4 Image Representation and Filtering
- M: Linear and non-linear filtering
- N: Edge detection and image feature enhancement
Chapter 5 Multi-scale Image Representation
- M: Scale-space theory
- N: Vascular image enhancement
Chapter 6 Multi-resolution Image Representation
- M: Wavelets and other decompositions
- N: Medical image compression
Chapter 7 Multi-dimensional Image Representation
- M: Tensor image representation & analysis
- N: Diffusion tensor imaging
Feature Detection, Analysis and Tracking
Chapter 8 Feature Detection and Texture Analysis
- M: Computer aided diagnosis
- N: Mammographic analysis
Chapter 9 Tracking of Salient Points
- M: Object detection, Kalman filter
- N: Instrument tracking in IR
Image Segmentation
Chapter 10 Clustering-based Segmentation
- M: Voxel-based clustering with bias correction
- N: Brain tissue segmentation
Chapter 11 Statistical Model-based Segmentation
- M: Statistical Shape Models
- N: Cardiac image analysis
Chapter 12 Implicit Model-based Segmentation
- M: PDE-based segmentation
- N: Segmentation of cortical surfaces
Chapter 13 Segmentation of Tree-like Structures
- M: Graph-cut based segmentation
- N: Segmentation of airways and vessels
Image Registration
Chapter 14 Points and Surface Registration
- M: Iterative closest point and variants
- N: Pre-operative to intra-operative mapping
Chapter 15 Graph Matching
- M: Registration of surfaces/trees with imperfect match
- N: Multi-subject registration of brain vasculature
Chapter 16 Volumetric Rigid Registration
- M: Introduce registration metrics and registration error measures
- N: Radiotherapy planning
Chapter 17 Volumetric Non-Rigid Registration
- M: Introduce registration metrics and registration error measures
- N: Construction of population atlases of organs
Chapter 18 Image Mosaicking
- M: Challenges of registration for mosaicking
- N: Endoscopic imaging for colonoscopy or similar
Motion, Deformation and Growth Analysis
Chapter 19 Motion Recovery and Analysis
- M: Optical flow and variants
- N: Motion from cardiac MR tagging
Chapter 20 Deformation Recovery and Analysis
- M: Models of tissue deformation (e.g. sliding deformation)
- N: Respiratory and cardiac motion analysis
Chapter 21 Development, degeneration, and growth analysis
- M: Statistics in Non-Euclidean Spaces: need and rationale
- N: Quantification of brain ageing and tumour growth
Medical Image Analysis in Large Scale Databases
Chapter 22 Detection and counting in large databases
- M: Machine learning methods – part I Bayesian inference
- N: Cell detection and counting in microscopy
Chapter 23 Segmentation and classification
- M: Machine learning methods – part II Deep learning
- N: Cell segmentation and tissue classification in digital pathology
Chapter 24 Population atlases and variability
- M: Manifold learning methods
- N: Atlas of brain development and ageing
Design and Validation of Medical Image Analysis Methods
Chapter 25 Designing Translational Solutions in Medical Image Analysis
- Introduction to the Stanford Biodesign Process
Chapter 26 Assessment of Medical Image Computing Methods
Overview of metrics and validation strategies in medical image computing covering segmentation, registration, learning
Details
- No. of pages:
- 935
- Language:
- English
- Copyright:
- © Academic Press 2021
- Published:
- 1st January 2021
- Imprint:
- Academic Press
- Paperback ISBN:
- 9780128136577
About the Authors
Alejandro Frangi
Alejandro (Alex) Frangi is Professor of Biomedical Image Computing at the University of Sheffield (USFD) and affiliated to the Electronic & Electrical Engineering Department. He is also Director of the Center for Computational Imaging and Simulation Technologies in Biomedicine and member of INSIGNEO Institute for in silico Medicine. Prof Frangi is Fellow of IEEE. His main research interests are in medical image computing, medical imaging and image-based computational physiology. Prof. Frangi has edited a book, published 5 editorial articles and over 90 journal papers in key international journals of his research field, as well as more than 120 book chapters and international conference papers. He has twice been Guest Editor of special issues of IEEE Trans on Medical Imaging, one on IEEE Trans Biomed Eng, and one of Medical Image Analysis journal.
Affiliations and Expertise
Professor of Biomedical Image Computing, University of Sheffield, UK
Jerry Prince
Jerry L. Prince has research interests in image processing and computer vision with primary application to medical imaging. He has studied and developed methods for imaging motion in the heart, tongue, and brain using magnetic resonance imaging. He has applied both statistical estimation and computer vision methods to the analysis of brain structure with applications in normal aging, Alzheimer’s disease, and multiple sclerosis. He is also co founder of Diagnosoft, Inc., a medical imaging software company. He received the BS degree from the University of Connecticut in 1979 and the S.M., E.E., and PhD degrees in 1982, 1986, and 1988, respectively, from the Massachusetts Institute of Technology, all in electrical engineering and computer science.
Affiliations and Expertise
Professor of Electrical and Computer Engineering, Johns Hopkins University, USA
Milan Sonka
Dr. Milan Sonka, Ph.D., is the Co-Founder of VIDA Diagnostics, Inc. Dr. Sonka has been a Professor of Electrical and Computer Engineering at University of Iowa since 2000, Ophthalmology and Visual Sciences since 2006, Applied Mathematical and Computational Sciences since 2001 and Radiation Oncology since 2006. He also serves as Co-director of Iowa Institute for Biomedical Imaging at the University of Iowa. He served as an Assistant Professor at Department of Control Engineering, Czech Technical University of Prague from 1984 to 1990. He served as Visiting Assistant Professor at Department of Electrical and Computer Engineering, The University of Iowa from 1990 to 1993. He served as Visiting Associate Professor from 1993 to 1994 and Associate Professor from 1994 to 2000. Dr. Sonka is a well-known scholar in the area of quantitative medical image analysis with a record of successful commercialization of his cardiovascular and pulmonary image analysis methods and approaches.
Affiliations and Expertise
Professor of Electrical and Computer Engineering, The University of Iowa, USA
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