Computer Vision For Biomedical Image Applications

With the rapid increase in the variety and quantity of biomedical images in recent years, we see a steadily growing number of computer vision technologies applied to biomedical applications. The time is ripe for us to take a closer look at the accomplishments and experiences gained in this research subdomain, and to strategically plan the directions of our future research. The scientific goal of our workshop, “Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends” (CVBIA), is to examine the diverse applications of computer vision to biomedical image applications, considering both current methods and promising new trends. An additional goal is to provide the opportunity for direct interactions between (1) prominent senior researchers and young scientists, including students, postdoctoral associates and junior faculty; (2) local researchers and international leaders in biomedical image analysis; and (3) computer scientists and medical practitioners. Our CVBIA workshop had two novel characteristics: each contributed paper was authored primarily by a young scientist, and the workshop attracted an unusually large number of well-respected invited speakers (and their papers). We had the good fortune of having Dr. Ayache of INRIA, France to talk about “Computational Anatomy and Computational Physiology,” Prof. Grimson of MIT to discuss “Analyzing Anatomical Structures: Leveraging Multiple Sources of Knowledge,” Dr. Jiang of the Chinese Academy of Sciences to present their work on “Computational Neuroanatomy and Brain Connectivity,” Prof.

The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs.

This book constitutes the refereed proceedings of the First International Workshop on Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends, CVBIA 2005, held in Beijing, China, in October 2005 within the scope of ICCV 20.

In modern medicine, imaging is the most effective tool for diagnostics, treatment planning and therapy. Almost all modalities have went to directly digital acquisition techniques and processing of this image data have become an important option for health care in future. This book is written by a team of internationally recognized experts from all over the world. It provides a brief but complete overview on medical image processing and analysis highlighting recent advances that have been made in academics. Color figures are used extensively to illustrate the methods and help the reader to understand the complex topics.

Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.

A comprehensive guide to understanding and interpreting digital images in medical and functional applications Biomedical Image Understanding focuses on image understanding and semantic interpretation, with clear introductions to related concepts, in-depth theoretical analysis, and detailed descriptions of important biomedical applications. It covers image processing, image filtering, enhancement, de-noising, restoration, and reconstruction; image segmentation and feature extraction; registration; clustering, pattern classification, and data fusion. With contributions from experts in China, France, Italy, Japan, Singapore, the United Kingdom, and the United States, Biomedical Image Understanding: Addresses motion tracking and knowledge-based systems, two areas which are not covered extensively elsewhere in a biomedical context Describes important clinical applications, such as virtual colonoscopy, ocular disease diagnosis, and liver tumor detection Contains twelve self-contained chapters, each with an introduction to basic concepts, principles, and methods, and a case study or application With over 150 diagrams and illustrations, this bookis an essential resource for the reader interested in rapidly advancing research and applications in biomedical image understanding.

Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future. Gives state-of-the-art methods in (bio)medical image synthesis Explains the principles (background) of image synthesis methods Presents the main applications of biomedical image synthesis methods

Our goal is to develop automated methods for the segmentation of thr- dimensional biomedical images. Here, we describe the segmentation of c- focal microscopy images of bee brains (20 individuals) by registration to one or several atlas images. Registration is performed by a highly parallel imp- mentation of an entropy-based nonrigid registration algorithm using B-spline transformations. We present and evaluate different methods to solve the cor- spondence problem in atlas based registration. An image can be segmented by registering it to an individual atlas, an average atlas, or multiple atlases. When registering to multiple atlases, combining the individual segmentations into a ?nalsegmentationcanbeachievedbyatlasselection,ormulticlassi?erdecision fusion. Wedescribeallthesemethodsandevaluatethesegmentationaccuracies that they achieve by performing experiments with electronic phantoms as well as by comparing their outputs to a manual gold standard. The present work is focused on the mathematical and computational t- ory behind a technique for deformable image registration termed Hyperelastic Warping, and demonstration of the technique via applications in image regist- tion and strain measurement. The approach combines well-established prin- ples of nonlinear continuum mechanics with forces derived directly from thr- dimensional image data to achieve registration. The general approach does not require the de?nition of landmarks, ?ducials, or surfaces, although it can - commodate these if available. Representative problems demonstrate the robust and ?exible nature of the approach. Three-dimensional registration methods are introduced for registering MRI volumes of the pelvis and prostate. The chapter ?rst reviews the applications, xi xii Preface challenges, and previous methods of image registration in the prostate.