Advances in Medical Image Computing

T. Tolxdorff; T. M. Deserno; H. Handels; H.-P. Meinzer

doi:10.1055/s-0038-1627070

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2009; 48(04): 311-313
DOI: 10.1055/s-0038-1627070

Editorial

Schattauer GmbH

Advances in Medical Image Computing

Autoren

T. Tolxdorff

¹Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Berlin, Germany
T. M. Deserno

²Department of Medical Informatics, Aachen University of Technology (RWTH), Aachen, Germany
H. Handels

³Department of Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
H.-P. Meinzer

⁴Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany

Abstract

Summary

Objectives: Medical image computing has become a key technology in high-tech applications in medicine and an ubiquitous part of modern imaging systems and the related processes of clinical diagnosis and intervention. Over the past years significant progress has been made in the field, both on methodological and on application level. Despite this progress there are still big challenges to meet in order to establish image processing routinely in health care. In this issue, selected contributions of the German Conference on Medical Image Processing (BVM) are assembled to present latest advances in the field of medical image computing.

Methods: The winners of scientific awards of the German Conference on Medical Image Processing (BVM) 2008 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, seven excellent papers were selected to describe important aspects of recent advances in the field of medical image processing.

Results: The selected papers give an impression of the breadth and heterogeneity of new developments. New methods for improved image segmentation, non-linear image registration and modeling of organs are presented together with applications of image analysis methods in different medical disciplines. Furthermore, state-of-the-art tools and techniques to support the development and evaluation of medical image processing systems in practice are described.

Conclusions: The selected articles describe different aspects of the intense development in medical image computing. The image processing methods presented enable new insights into the patient’s image data and have the future potential to improve medical diagnostics and patient treatment.

Keywords

Medical image computing - image segmentation - non-linear image registration - image analysis - modeling of organs - software development - medical informatics

Volltext

Referenzen

References
1 Handels H, Ehrhardt J. Medical Image Computing for Computer Supported Diagnostics and Therapy – Advances and Perspectives. Methods Inf Med 2009; 48: 11-17.
2 Lehmann TM, Aach T, Witte H. Sensor, Signal and Image Informatics, State of the Art and Current Topics. Methods Inf Med 2006; 47 S1 57-67.
3 Horsch A, Deserno TM, Handels H, Meinzer HP, Tolxdorff T. IJCARS Special Issue Editorial, BVM 2007 German Conference on Medical Image Processing. Int J CARS 2008; 2: 253-254.
4 Handels H, Horsch A, Meinzer HP. Advances in Medical Image Computing. Methods Inf Med 2007; 46: 251-253.
5 Lehmann TM, Meinzer HP, Tolxdorff T. Advances in Biomedical Image Analysis, Past, present and future challenges. Methods Inf Med 2004; 43 (Suppl. 04) 308-314.
6 Werner R, Ehrhardt J, Frenzel T, Säring D, Lu W, Low D, Handels H. Motion Artifact Reducing Reconstruction of 4D CT Image Data for the Analysis of Respiratory Dynamics. Methods Inf Med 2007; 46 (Suppl. 03) 254-260.
7 Oehler M, Buzug TM. Statistical Image Reconstruction for Inconsistent CT Projection Data. Methods Inf Med 2007; 46 (Suppl. 03) 261-269.
8 Kabus S, Franz A, Fischer B. Spatially Varying Elasticity in Image Registration. Methods Inf Med 2007; 46 (Suppl. 03) 287-291.
9 Ehrhardt J, Säring D, Handels H. Structure-preserving Interpolation of Temporal and Spatial Image Sequences Using an Optical Flow Based Method. Methods Inf Med 2007; 46 (Suppl. 03) 300-307.
10 Haber E, Modersitzki J. Intensity Gradient Based Registration and Fusion of Multi-modal Images. Methods Inf Med 2007; 46 (Suppl. 03) 292-299.
11 Heimann T, Wolf I, Meinzer HP. Automatic Generation of 3D Statistical Shape Models with Optimal Landmark Distributions. Methods Inf Med 2007; 46 (Suppl. 03) 275-281.
12 von Berg J, Lorenz C. A Geometric Model of the Beating Heart. Methods Inf Med 2007; 46 (Suppl. 03) 282-286.
13 Hufnagel H, Pennec X, Ehrhardt J, Ayache N, Handels H. Computation of a Probabilistic Statistical Shape Model Optimizing a Global Criterion. Methods Inf Med 2009; 48: 314-319.
14 Wörz S, Winz ML, Rohr K. Geometric Alignment of 2D Gel Electrophoresis Images. Methods Inf Med 2009; 48: 320-323.
15 Münzenmayer C, Kage A, Mühldorfer S, Wittenberg T. Computer-assisted Diagnosis for Precancerous Lesions in the Esophagus. Methods Inf Med 2009; 48: 324-330.
16 Apelt D, Strasburger H, Rascher-Friesenhausen R, Klein J, Preim B. Generalizing the Evaluation of Medical Image Processing Tools by Use of Gabor Patterns. Methods Inf Med 2009; 48: 331-335.
17 Neuhaus J, Maleike D, Nolden M, Kenngott HG, Meinzer HP, Wolf I. A Quality-refinement Process for Medical Imaging Applications. Methods Inf Med 2009; 48: 336-339.
18 Säring D, Relan J, Groth M, Müllerleile K, Handels H. 3D Segmentation of the Left Ventricle combining Long-and Short-axis MR Images. Methods Inf Med 2009; 48: 340-343.
19 Schmidt-Richberg A, Handels H, Ehrhardt J. Integrated Segmentation and Non-linear Registration for Organ Segmentation and Motion Field Estimation in 4D CT Data. Methods Inf Med 2009; 48: 344-349.