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Imaging techniques have been used for decades to detect anomalies, study organ function and for diagnostic purposes. Advances in imaging techniques and image processing as well as a wider availability of lung imaging is providing an increasing amount of data and new insights into lung structure and function and their alterations in common lung diseases. Functional imaging biomarkers have the potential to better characterize individual patient phenotypes, predict disease trajectories, and help personalize therapy. The wealth of new data also confronts us with new challenges in terms of identifying, quantifying, deciphering, and standardizing image-based parameters pertaining to regional lung function.
This volume contains the papers presented at the 14th International Conference on Information Processing in Medical Imaging. IPMI meetings have a a strong emphasis on the clinical relevance and validation of medical imaging. This book covers the whole spectrum: acquisition, tomographic reconstruction, registration, segmentation, knowledge-based analysis, display and image quality as well as several important applications. Several papers present significant advances in topics already discussed at previous meetings while others deal with new topics and methodology, opening new horizons in medical imaging. In addition to the 28 full-length papers, 30 short communications are included to sample the most current work in progress. Audience: An up-to-date and complete overview of ongoing research in medical imaging, beneficial to all physicists, computer scientists and physicians who wish to remain informed on state-of-the-art methodology in medical imaging.
Respiratory motion causes an important uncertainty in radiotherapy planning of the thorax and upper abdomen. The main objective of radiation therapy is to eradicate or shrink tumor cells without damaging the surrounding tissue by delivering a high radiation dose to the tumor region and a dose as low as possible to healthy organ tissues. Meeting this demand remains a challenge especially in case of lung tumors due to breathing-induced tumor and organ motion where motion amplitudes can measure up to several centimeters. Therefore, modeling of respiratory motion has become increasingly important in radiation therapy. With 4D imaging techniques spatiotemporal image sequences can be acquired to i...
This book constitutes the refereed proceedings of the 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011, held at Kloster Irsee, Germany, in July 2011. The 24 full papers and 39 poster papers included in this volume were carefully reviewed and selected from 224 submissions. The papers are organized in topical sections on segmentation, statistical methods, shape analysis, registration, diffusion imaging, disease progression modeling, and computer aided diagnosis. The poster sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.
Medical image analysis focuses on the extraction of meaningful information from medical images in order to facilitate clinical assessment, diagnostics and treatment. Image processing techniques have gradually become an essential part of the modern health care system, a consequence of the continuous technological improvements and the availability of a variety of medical imaging techniques. Magnetic Resonance Imaging (MRI) is an imaging technique that stands out as non-invasive, highly versatile, and capable of generating high quality images without the use of ionizing radiation. MRI is frequently performed in the clinical setting to assess the morphology and function of the heart and vessels....
This book constitutes the refereed proceedings of the 17th International Conference on Information Processing in Medical Imaging, IPMI 2001, held in Davis, CA, USA, in June 2001. The 54 revised papers presented were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on objective assessment of image quality, shape modeling, molecular and diffusion tensor imaging, registration and structural analysis, functional image analysis, fMRI/EEG/MEG, deformable registration, shape analysis, and analysis of brain structure.
Medical imaging and medical image analysisare rapidly developing. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical,...
Brain Warping is the premier book in the field of brain mapping to cover the mathematics, physics, computer science, and neurobiological issues related to brain spatial transformation and deformation correction. All chapters are organized in a similar fashion, covering the history, theory, and implementation of the specific approach discussed for ease of reading. Each chapter also discusses the computer science implementations, including descriptions of the programs and computer codes used in its execution. Readers of Brain Warping will be able to understand all of the approaches currently used in brain mapping, incorporating multimodality, and multisubject comparisons.Key Features* The only book of its kind* Subject matter is the fastest growing area in the field of brain mapping* Presents geometrically-based approaches to the field of brain mapping* Discusses intensity-based approaches to the field of brain mapping
Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Beyond medical image computing, the methods described in this book may also apply to other domains such as sign...