Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis
Book Citation Index

Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis

Kenji Suzuki (University of Chicago, USA)
Release Date: January, 2012|Copyright: © 2012 |Pages: 524
DOI: 10.4018/978-1-4666-0059-1
ISBN13: 9781466600591|ISBN10: 1466600594|EISBN13: 9781466600607
Hardcover:
Available
$245.00
TOTAL SAVINGS: $245.00
Benefits
  • Printed-On-Demand (POD)
  • Usually ships one day from order
E-Book:
(Multi-User License)
Available
$245.00
TOTAL SAVINGS: $245.00
Benefits
  • Multi-user license (no added fee)
  • Immediate access after purchase
  • No DRM
  • ePub with PDF download
Hardcover +
E-Book:
(Multi-User License)
Available
$295.00
TOTAL SAVINGS: $295.00
Benefits
  • Printed-On-Demand (POD)
  • Usually ships one day from order
  • Multi-user license (no added fee)
  • Immediate access after purchase
  • No DRM
  • ePub with PDF download
OnDemand:
(Individual Chapters)
Available
$37.50
TOTAL SAVINGS: $37.50
Benefits
  • Purchase individual chapters from this book
  • Immediate PDF download after purchase or access through your personal library
Description & Coverage
Description:

Medical imaging is an indispensable tool for modern healthcare. Machine leaning plays an essential role in the medical imaging field, with applications including medical image analysis, computer-aided diagnosis, organ/lesion segmentation, image fusion, image-guided therapy, and image annotation and image retrieval.

Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis provides a comprehensive overview of machine learning research and technology in medical decision-making based on medical images. This book covers major technical advancements and research findings in the field of Computer-Aided Diagnosis (CAD). As it demonstrates the practical applications of CAD, this book is a useful reference for professors in engineering and medical schools, students in engineering and applied-science, medical students, medical engineers, researchers in industry, academia, and health science, radiologists, cardiologists, surgeons, and healthcare professionals.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Automated Segmentation Computed Tomography Scans
  • Clinical Machine Learning
  • Computer-aided detection and diagnosis
  • Content-Based Image Retrieval
  • Ensemble Learning
  • Fuzzy Methods for Image Analysis
  • Machine Learning Techniques
  • Magnetic resonance imaging
  • Medical Image Registration, Segmentation and Classification
  • Relevance feedback
Reviews and Testimonials

Machine learning is the core theme of most CAD methodologies. This book covers the principles and techniques of machine learning as applied to specific CAD applications in a variety of diseases and body regions, and for different imaging modalities. Twenty leading research groups in the field contributed chapters to this book covering applications in breast, lung, brain, abdomen, and whole-body imaging. This book is an excellent source of the state-of-the-art accomplishments in machine learning and CAD in one and readily accessible form. Teachers, students, practicing engineers, physicians, imaging scientists, and technologists who intend to learn about this extremely important and revolutionary area in medicine may all benefit from the wealth of information contained in this compendium.

– Jayaram K. Udupa
Table of Contents
Search this Book:
Reset
Editor/Author Biographies
Kenji Suzuki received his B.S. and M.S. degrees in engineering from Meijo University, Japan, in 1991 and 1993, respectively, and his Ph.D. degree (by Published Work) in engineering from Nagoya University, Japan, in 2001. From 1993 to 2001, he worked at Hitachi Medical Corporation, and then Aichi Prefectural University as faculty. In 2001, he joined Department of Radiology at The University of Chicago, as Research Associate. Since 2006, he has been Assistant Professor of Radiology, Medical Physics, and Cancer Research Center there. Dr. Suzuki’ research interests include computer-aided diagnosis and machine learning. He has published more than 190 papers (including 70 peer-reviewed journal papers), 4 books, and 13 book chapters, and edited 4 journal special issues. He has an h-index of 22 as of 2011. He was awarded more than 25 grants including NIH R01 grants. He has been serving as the Editor-in-Chief and an Associate Editor of 13 leading international journals, including Medical Physics, Academic Radiology, and Algorithms. He has been serving as a referee for more than 45 international journals, an organizer of 5 international conferences, and a program committee member of 50 international conferences. He had supervised/co-supervised more than 60 graduate/undergraduate students, postdocs/computer scientists, and visiting professors. He has received numerous awards, including a University of Chicago Paul C. Hodges Award, three Certificate of Merit Awards and Research Trainee Prize from RSNA, Young Investigator Award from Cancer Research Foundation, an IEEE Outstanding Member Award, and Honorable Mention Poster Award at SPIE International Symposium on Medical Imaging. He has been a Senior Member of IEEE since 2004.
Editorial Policy
In order to ensure the highest ethical practices are achieved for each book, IGI Global provides a full document of policies and guidelines that all editors, authors, and reviewers are expected to follow. View Full Editorial Policy
Peer Review Process
The peer review process is the driving force behind all IGI Global books and journals. All IGI Global reviewers maintain the highest ethical standards and each manuscript undergoes a rigorous double-blind peer review process, which is backed by our full membership to the Committee on Publication Ethics (COPE). The full publishing process and peer review are conducted within the IGI Global eEditorial Discovery® online submission system and on average takes 30 days. Learn More
Ethics & Malpractice
IGI Global affirms that ethical publication practices are critical to the successful development of knowledge. Therefore, it is the policy of IGI Global to maintain high ethical standards in all publications. These standards pertain to all books, journals, chapters, and articles accepted for publication. This is in accordance with standard scientific principles and IGI Global’s position as a source of scientific knowledge. Learn More
Archiving
All of IGI Global's content is archived via the CLOCKSS and LOCKSS initiative. Additionally, all IGI Global published content is available in IGI Global's InfoSci® platform.
Editorial Review Board
  • Issam El Naqa, McGill University, Canada
  • Hiroshi Fujita, Gifu University, Japan
  • Maryellen L. Giger, University of Chicago, USA
  • Steve B. Jiang, University of California San Diego, USA
  • Rangaraj M. Rangayyan, University of Calgary, Canada
  • Daniel Rueckert, Imperial College London, UK