Medical Image Computing and Computer-Assisted Intervention – by Antoine Grigis, Vincent Noblet, Fréderic Blanc, Fabrice
By Antoine Grigis, Vincent Noblet, Fréderic Blanc, Fabrice Heitz, Jérome de Seze (auth.), Gabor Fichtinger, Anne Martel, Terry Peters (eds.)
The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed court cases of the 14th overseas convention on clinical snapshot Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. in keeping with rigorous peer experiences, this system committee conscientiously chosen 251 revised papers from 819 submissions for presentation in 3 volumes. the second one quantity comprises eighty three papers prepared in topical sections on diffusion weighted imaging, fMRI, statistical research and form modeling, and registration.
Read or Download Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part II PDF
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Additional info for Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part II
Sample text
Aleksandar Petrovi´c and Lilla Z¨ ollei Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model . . . . . . . . . . . . . . . . . . . . Ali Gooya, Kilian M. Pohl, Michel Bilello, George Biros, and Christos Davatzikos Non-local Shape Descriptor: A New Similarity Metric for Deformable Multi-modal Registration . . . . . . . . . . . . . . . . . . . . . Mattias P. Heinrich, Mark Jenkinson, Manav Bhushan, Tahreema Matin, Fergus V.
This step is not straightforward since we need to ensure that all the tensors are drawn from the same distribution. A natural idea is to learn tensor distribution at each voxel by considering all the tensors in a surrounding user-defined spatial neighbourhood. This is based on the commonly made assumption of a constant piecewise model, with known limitations, in particular, at the interface between tissues. fr G. Fichtinger, A. Martel, and T. ): MICCAI 2011, Part II, LNCS 6892, pp. 1–8, 2011. c Springer-Verlag Berlin Heidelberg 2011 2 A.
DTs are then estimated from the DW images using a least square algorithm. Then, a fiber tract extraction is performed with Slicer2 (Label Seeding module) exploiting the first exam information. 1). 2). The DTs of the second exam are warped using a linear interpolation in the Log-eulidean space and the preservation of principal direction (PPD) reorientation strategy to be aligned with the first exam [5]. To this end, an affine and a Bspline based transformation are estimed from the FA images of the two acquisitions [5].