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Advances in Medical Image Computing
20 January 2018 (online)
Objectives: Medical image computing has become a key technology in high-tech applicationsin medicine. Nowadays, medical image computing techniques are applied in daily routine in various medical disciplines. However, further developments are needed to improve computer-aided diagnoses and computer-assisted therapy planning and performance in the future. 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 Conferences on Medical Image Processing (BVM) 2005 and 2006 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, eleven excellent papers were selected to describe important aspects of recent advances in the field of medical image computing.
Results: The selected papers give an impression of the broad range and heterogeneity of new developments in the field of medical image computing. New methods for improved image reconstruction, image segmentation, modelling of organs, as well as methodical improvements of non-linear image registration algorithms are presented together with applications of image analysis methods in different medical disciplines.
Conclusions: The selected articles describe different aspects of the intense development in medical image computing. The image computing methods presented enable new insights into the patient’s image data and have the future potential to improve medical diagnostics and patient treatment.
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- 2 Pfeifer B, Fischer G, Hanser F, Seger M, Hintermüller C, Modre-Osprian R. et al. Atrial and Ventricular Myocardium Extraction Using Modelbased Techniques. Methods Inf Med 2006; 45 (01) 19-26.
- 3 Handels H, Werner R, Frenzel T, Säring D, Lu W, Low D. et al. Generation of 4D CT Image Data and Analysis of Lung Tumour Mobility During the Breathing Cycle. Hasman A, Haux R, van der Lei J, Clercq E, France FHR. Medical Informatics Europe 2006, MIE 2006 – Ubiquity: Technologies for Better Health in Aging Societies. 2006. IOS Press; 2006: 977-982.
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- 5 Werner R, Ehrhardt J, Frenzel T, Säring D, Lu W, Low D. et al. Motion Artifact Reducing Reconstruction of 4D CT Image Data for the Analysis of Respiratory Dynamics. Methods Inf Med 2007; 46: 254-260.
- 6 Oehler M, Buzug TM. Statistical Image Reconstruction for Inconsistent CT Projection Data. Methods Inf Med 2007; 46: 261-269.
- 7 Beyer J, Langer C, Fritz L, Hadwiger M, Bühler K. Interactive Diffusion Based Smoothing and Segmentation of Volumetric Datasets on Graphics Hardware. Methods Inf Med 2007; 46: 270-274.
- 8 Heimann T, Wolf I, Meinzer HP. Automatic Generation of 3D Statistical Shape Models with Optimal Landmark Distributions. Methods Inf Med 2007; 46: 275-281.
- 9 von Berg J, Lorenz C. A Geometric Model of the Beating Heart. Methods Inf Med 2007; 46: 282-286.
- 10 Kabus S, Franz A, Fischer B. Spatially Varying Elasticity in Image Registration. Methods Inf Med 2007; 46: 287-291.
- 11 Haber E, Modersitzki J. Intensity Gradient Based Registration and Fusion of Multi-modal mages. Methods Inf Med 2007; 46: 292-299.
- 12 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: 300-307.
- 13 Kier C, Meyer-Wiethe K, Seidel G, Aach T. Ultrasound Cerebral Perfusion Analysis Based on a Mathematical Model for Diminution Harmonic Imaging. Methods Inf Med 2007; 46: 308-313.
- 14 Bell A, Würflinger T, Ropers S-O, Boecking A, Aach T. Towards Fully Automatic Acquisition of Mul-timodal Cytopathological Microscopy Images with Autofocus, Scene Matching and Registration. Methods Inf Med 2007; 46: 314-323.
- 15 Chaisaowong K, Jäger P, Knepper A, Kraus T, Aach T. Computer-Assisted Diagnosis for Early Stage Pleural Mesothelioma: Towards Automated Detection and Quantitative Assessment of Pleural Thickenings from Thoracic CT Images. Methods Inf Med 2007; 46: 324-331.
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- 17 Handels H, Werner R, Frenzel T, Säring D, Lu W, Low D. et al. Improved Reconstruction of 4D MSCT Image Data and Motion Analysis of Lung Tumors Using Non-linear Registration Methods. Kim SI, Suh TS. World Congress on Medical Physics and Biomedical Engineering 2006; 2006. Seoul, Korea. Berlin: Springer Verlag; 2006: 2172-2175.
- 18 Ehrhardt J, Handels H, Plötz W, Pöppl SJ. Atlas-based Recognition of Anatomical Structures and Landmarks and the Automatic Computation of Orthopedic Parameters. Methods Inf Med 2004; 43 (04) 391-397.
- 19 Stefanescu R, Pennec N, Ayache N. A Grid Service for the Interactive Use of a Parallel Nonrigid Registration Algorithm of Medical Images. Methods Inf Med 2005; 44 (02) 239-243.
- 20 Marschollek M, Teistler M, Bott OJ, Stuermer KM, Pretschner DP, Dresing K. Pre-operative Dynamic Interactive Exploration of Complex Articular Fractures Using a Novel 3D Navigation Tool. Methods Inf Med 2006; 45 (04) 384-348.