By Zhong Xue, Stephen T. C. Wong (auth.), Hongen Liao, Cristian A. Linte, Ken Masamune, Terry M. Peters, Guoyan Zheng (eds.)
This e-book constitutes the refereed court cases of 2 workshops MAIR/AE-CAI 2013, held together with MICCAI 2013, held in Nagoya, Japan, in September 2013. The 29 revised complete papers awarded have been conscientiously reviewed and chosen from forty four submissions. The papers hide a variety of subject matters addressing the most examine efforts within the fields of clinical photo formation, research and interpretation, augmented truth and visualization, desktop assisted intervention, interventional imaging, image-guided robotics, image-guided intervention, surgical making plans and simulation, systematic additional- and intra-corporeal imaging modalities, and normal organic and neuroscience photograph computing.
Read or Download Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions: 6th International Workshop, MIAR 2013 and 8th International Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings PDF
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Additional resources for Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions: 6th International Workshop, MIAR 2013 and 8th International Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings
The stereo cameras are ﬁrst placed at a distance of about 11cm to the phantom. They are then manually moved at a speed of about 10mm/s and held still at four locations. Simultaneous Tracking, 3D Reconstruction and Deforming Point Detection 41 It should be noted that our tracking system selects the ﬁrst frame of the left camera as the world coordinate and the OptiTrack system has a diﬀerent world coordinate. To enable the comparison of trajectories from these two different coordinate systems, the Euclidean transformation between them need to be calculated.
By combination of both eigenvalues, the resulting response is calculated by  0 2 S 2 ( x;σ ) R ( x;σ ) V ( x;σ ) = 1 − exp − exp − 2 2β 2c 2 where R( x;σ ) = λ1( x;σ ) S (x ; σ ) = λ (x; σ ) + λ 2 1 2 2 if λ2 ( x;σ ) > 0 otherwise, (2) λ2 ( x;σ ) measures the blobness of each pixel in the image, ( x ; σ ) defines the local second-order structureness of each Automatic and Real-Time Identification of Breathing Pattern 31 pixel, and β and c decide the sensitivity of the line filter to both measures R ( x ; σ ) and S ( x ; σ ) .
The center of the four MV points, AV, and apex points were then used to compute an initial similarity transform for the mean shape ¯ total ). The mean shape was overlaid with the TEE of the total heart model(X image and small adjustments could be made to the landmark points to obtain an optimal initial pose of the model. Using this transformation as a starting point, the manual outlining was, performed by two observers, in ten equally distributed 2D short axis views and 4 long axis views, obtained by slicing the TEE image ¯ total were interpolated by Band the mean shape model.
Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions: 6th International Workshop, MIAR 2013 and 8th International Workshop, AE-CAI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings by Zhong Xue, Stephen T. C. Wong (auth.), Hongen Liao, Cristian A. Linte, Ken Masamune, Terry M. Peters, Guoyan Zheng (eds.)