Milan Sonka

Gary E. Christensen

The nineteenth biennial International Conference on Information Processing in Medical Imaging (IPMI) was held July 11–15, 2005 in Glenwood Springs, CO, USA on the Spring Valley campus of the Colorado Mountain College. Following the successful meeting in beautiful Ambleside in England, this year’s conference addressed important recent developments in a broad range of topics related to the acquisition, analysis and application of biomedical images. Interest in IPMI has been steadily growing over the last decade. This is p- tially due to the increased number of researchers entering the ?eld of medical imagingasaresultoftheWhitakerFoundationandtherecentlyformedNational Institute of Biomedical Imaging and Bioengineering. This year, there were 245 full manuscripts submitted to the conference which was twice the number s- mitted in 2003 and almost four times the number of submissions in 2001. Of these papers, 27 were accepted as oral presentations, and 36 excellent subm- sions that could not be accommodated as oral presentations were presented as posters. Selection of the papers for presentation was a di?cult task as we were unable to accommodate many of the excellent papers submitted this year. All accepted manuscripts were allocated 12 pages in these proceedings.

19th International Conference, IPMI 2005, Glenwood Springs, CO, USA, July 10-15, 2005, Proceedings

Information Processing in Medical Imaging

Jan Kybic

Ioannis A. Kakadiaris

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, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. We were enthusiastic when the organizers of the 2004 European Conference on Computer Vision (ECCV) allowed us to organize a satellite workshop devoted to medical image analysis.

ECCV 2004 Workshops CVAMIA and MMBIA Prague, Czech Republic, May 15, 2004, Revised Selected Papers

Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis

David Zhang

The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseris LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. In parallel to the printed book, each new volume is published electronically in LNCS Online.

Second International Conference, ICMB 2010, Hong Kong, China, June 28-30, 2010. Proceedings

Medical Biometrics

Jerry L. Prince

Alejandro F. Frangi

Medical imaging is increasingly at the base of many fundamental breakthroughs in biomedical sciences, becoming a fundamental enabling technology of biomedical scientific progress. This book presents practical knowledge on medical image computing and analysis Written by top educators and experts in the field, this text is a modern, practical, broad, and self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains, reflecting the nature of the discipline today. Learn: The core representations and properties of digital images and image enhancement techniques Advanced image computing methods including segmentation, registration, motion and shape analysis, and machine learning How medical image computing (MIC) is used in clinical and medical research How to identify alternative strategies and employ software tools to solve typical problems in MIC An authoritative description of key concepts and methods Tutorial-based sections that clearly explain the principles and their application to different medical domains Self-contained chapters provide flexibility, allowing the text to be used on courses with different structures A representative selection of topics to match a modern and relevant approach to medical image computing Focus on medical image computing as a technology to meet clinical needs

Milan Sonka

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