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Deep Learning for Medical Image Analysis
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
Medical Image Analysis
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. - An authoritati...
Mammographic Image Analysis
The key contribution of the approach to x-ray mammographic image analysis developed in this monograph is a representation of the non-fatty compressed breast tissue that we show can be derived from a single mammogram. The importance of the representation, called hint, is that it removes all those changes in the image that are due only to the particular imaging conditions (for example, the film speed or exposure time), leaving just the non-fatty 'interesting' tissue. Normalising images in this way enables them to be enhanced and matched, and regions in them to be classified more reliably, because unnecessary, distracting variations have been eliminated. Part I of the monograph develops a model-based approach to x-ray mammography, Part II shows how it can be put to work successfully on a range of clinically-important tasks, while Part III develops a model and exploits it for contrast-enhanced MRI mammography. The final chapter points the way forward in a number of promising areas of research.
Theory And Applications Of Image Analysis: Selected Papers From The 7th Scandinavian Conference On Image Analysis
This book contains 31 papers carefully selected from among those presented at the 7th Scandinavian Conference on Image Analysis. The authors have extended their papers to give a more in-depth discussion of the theory, or of the experimental validation of the method they have proposed. The topics covered are current and wide-ranging and include both 2D- and 3D-vision, and low to high level vision.
Front-End Vision and Multi-Scale Image Analysis
Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of...
Radiographic Image Analysis - E-Book
Learn to produce the most accurate radiographic images on the first try with Radiographic Image Analysis, 4th Edition. This thoroughly updated guide walks you through the steps of how to carefully evaluate an image, how to identify the improper positioning or technique that caused a poor image, and how to correct the problem. For each procedure, there is a diagnostic-quality radiograph along with several examples of unacceptable radiographs, a complete list of radiographic evaluation guidelines, and detailed discussions on how each of the evaluation points is related to positioning and technique. Each unacceptable radiograph is accompanied by a description of the misaligned anatomical struct...
Radiographic Image Analysis
This comprehensive guide shows how to reduce the need for repeat radiographs. It teaches how to carefully evaluate an image, how to identify the improper positioning or technique that caused a poor image, and how to correct the problem. This text equips radiographers with the critical thinking skills needed to anticipate and adjust for positioning and technique challenges before a radiograph is taken, so they can produce the best possible diagnostic quality radiographs. Provides a complete guide to evaluating radiographs and troubleshooting positioning and technique errors, increasing the likelihood of getting a good image on the first try. Offers step-by-step descriptions of all evaluation ...
Landmark-Based Image Analysis
This is the first comprehensive treatment of the extraction of landmarks from multimodality images and the use of these features for elastic image registration. The emphasis is on model-based approaches, i.e. on the use of explicitly represented knowledge in computer vision. Both geometric models (describing the shape of objects) and intensity models (directly representing the image intensities) are utilized. The work describes theoretical foundations, computational and algorithmic issues, as well as practical applications, notably in medicine (neurosurgery and radiology), remote sensing, and industrial automation. Connections with computer graphics and artificial intelligence are illustrated. Audience: This volume will be of interest to readers seeking an introduction and overview of landmark-based image analysis, and in particular to graduate students and researchers in computer science, engineering, computer vision, and medical image analysis.
Biomedical Image Analysis
Computers have become an integral part of medical imaging systems and are used for everything from data acquisition and image generation to image display and analysis. As the scope and complexity of imaging technology steadily increase, more advanced techniques are required to solve the emerging challenges. Biomedical Image Analysis demonstr
