A widely used, classroom-tested text, Applied Medical Image Processing: A Basic Course delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field. Avoiding excessive mathematical formalisms, the book presents key principles by implementing algorithms from scratch and usin

A widely used, classroom-tested text, Applied Medical Image Processing: A Basic Course delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field. Avoiding excessive mathematical formalisms, the book presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data and illustrations on an accompanying companion website. Organized as a complete textbook, it provides an overview of the physics of medical image processing and discusses imaging physics, clinical applications of image processing, image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, tomographic reconstruction and basic machine learning. This Third Edition of the bestseller: Contains a brand-new chapter on the basics of machine learning Devotes more attention to the subject of color space Includes additional examples from radiology, internal medicine, surgery, and radiation therapy Incorporates freely available programs in the public domain (e.g., GIMP, 3DSlicer, and ImageJ) when applicable Beneficial to students of medical physics, biomedical engineering, computer science, applied mathematics, and related fields, as well as medical physicists, radiographers, radiologists, and other professionals, Applied Medical Image Processing: A Basic Course, Third Edition is fully updated and expanded to ensure a perfect blend of theory and practice. Wolfgang Birkfellner studied theoretical physics at, and holds a Ph.D in medical physics from, the University of Vienna, Austria. Currently, he is heading the Digital Image Processing Laboratory at the Center for Biomedical Engineering and Physics at the Medical University of Vienna. He is also a reviewer and editorial board member for major journals in the field, program committee member for international conferences, and principal investigator for several third-party funded research projects. Previously, he served as senior researcher at the University Hospital Basel/Switzerland and associate professor of medical physics at the Center for Biomedical Engineering and Physics of Vienna Medical School.

To successfully detect and diagnose disease, it is vital for medical diagnosticians to properly apply the latest medical imaging technologies. It is a worrisome reality that due to either the nature or volume of some of the images provided, early or obscured signs of disease can go undetected or be misdiagnosed. To combat these inaccuracies, diagno

In recent years, the remarkable advances in medical imaging instruments have increased their use considerably for diagnostics as well as planning and follow-up of treatment. Emerging from the fields of radiology, medical physics and engineering, medical imaging no longer simply deals with the technology and interpretation of radiographic images. The limitless possibilities presented by computer science and technology, coupled with engineering advances in signal processing, optics and nuclear medicine have created the vastly expanded field of medical imaging. The Handbook of Medical Imaging is the first comprehensive compilation of the concepts and techniques used to analyze and manipulate medical images after they have been generated or digitized. The Handbook is organized in six sections that relate to the main functions needed for processing: enhancement, segmentation, quantification, registration, visualization as well as compression storage and telemedicine. * Internationally renowned authors(Johns Hopkins, Harvard, UCLA, Yale, Columbia, UCSF) * Includes imaging and visualization * Contains over 60 pages of stunning, four-color images

A widely used, classroom-tested text, Applied Medical Image Processing: A Basic Course delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field. Avoiding excessive mathematical formalisms, the book presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data and illustrations on an accompanying CD-ROM or companion website. Organized as a complete textbook, it provides an overview of the physics of medical image processing and discusses image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction. This Second Edition of the bestseller: Contains two brand-new chapters on clinical applications and image-guided therapy Devotes more attention to the subject of color space Includes additional examples from radiology, internal medicine, surgery, and radiation therapy Incorporates freely available programs in the public domain (e.g., GIMP, 3DSlicer, and ImageJ) when applicable Beneficial to students of medical physics, biomedical engineering, computer science, applied mathematics, and related fields, as well as medical physicists, radiographers, radiologists, and other professionals, Applied Medical Image Processing: A Basic Course, Second Edition is fully updated and expanded to ensure a perfect blend of theory and practice.

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Hands-on text for a first course aimed at end-users, focusing on concepts, practical issues and problem solving.