New PDF release: Medical Imaging and Augmented Reality: 4th International

By Terry M. Peters, Cristian A. Linte, John Moore, Daniel Bainbridge, Douglas L. Jones (auth.), Takeyoshi Dohi, Ichiro Sakuma, Hongen Liao (eds.)

ISBN-10: 3540799818

ISBN-13: 9783540799818

This booklet constitutes the refereed complaints of the 4th foreign Workshop on scientific Imaging and Augmented truth, MIAR 2008, held in Tokyo, Japan, in August 2008.

The forty four revised complete papers offered including three invited papers have been conscientiously reviewed and chosen from ninety submissions. The papers are equipped in topical sections on surgical making plans and simulation, scientific photograph computing, photo research, form modeling and morphometry, image-guided robotics, image-guided intervention, interventional imaging, photo registration, augmented fact, and picture segmentation.

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References 1. : Simulation and Prediction of Orthognathic Surgery on Skeletal Class III Deformation. In: 3rd Chinese International Congress on Oral and Maxillofacial Surgery, in conjunction with 6th National Congress on Oral and Maxillofacial Surgery, Kunming, Yunan, PRC, p. 338 (2002) 2. : Deformable Modeling of Facial Tissue for Craniofacial Surgery Simulation. Computer Aided Surgery 3, 228–238 (1998) 3. : Finite Element Modeling in Surgery Simulation. Proceedings of the IEEE 86(33), 490–503 (1998) 4.

In: Proc. of the 2005 IEEE International Conf. on Robotics and Automation, pp. 1646–1651 (2005) 23. : A Novel Approach to Robotic Cardiac Surgery Using Haptics and Vision. Cardiovascular Engineering: An International Journal 2(1), 15–21 (2002) 24. : A General Purpose 7 DOF Haptic Device: Applications Towards Robot-Assisted Surgery. IEEE/ASME Trans. on Mechatronics 12(6), 662–669 (2007) 25. : JND Analysis of Texture Roughness Perception Using a Magnetic Levitation Haptic Device. In: Proc. Of the 2nd Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp.

Quantitative validation results show that MTM can achieve the same accuracy as FEM, while nearly half the computation time [4]. Unfortunately, this comes at the expense of intensive pre-computation. In order to learn the prior knowledge of tissue deformation from the training samples, Meller, in 2005, has applied Statistical Deformable Model (SDM) for maxillofacial prediction [7]. To capture the basic shape and the major variations of a certain class, point distribution model has been established from the pre- and post- operative facial skins.

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Medical Imaging and Augmented Reality: 4th International Workshop Tokyo, Japan, August 1-2, 2008 Proceedings by Terry M. Peters, Cristian A. Linte, John Moore, Daniel Bainbridge, Douglas L. Jones (auth.), Takeyoshi Dohi, Ichiro Sakuma, Hongen Liao (eds.)


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