Medical Computer Vision. Recognition Techniques and by Chen-Rui Chou, Stephen Pizer (auth.), Bjoern H. Menze, Georg

By Chen-Rui Chou, Stephen Pizer (auth.), Bjoern H. Menze, Georg Langs, Le Lu, Albert Montillo, Zhuowen Tu, Antonio Criminisi (eds.)

This e-book constitutes the completely refereed workshop lawsuits of the second one foreign Workshop on scientific laptop imaginative and prescient, MCV 2012, held in great, France, October 2012 together with the fifteenth foreign convention on clinical snapshot Computing and laptop Assisted Intervention, MICCAI 2012.
The 24 papers were chosen out of forty two submissions. At MCV 2012, 12 papers have been offered as a poster and 12 as a poster including a plenary speak. The ebook additionally beneficial properties 4 chosen papers that have been provided on the earlier CVPR clinical machine imaginative and prescient workshop held together with the overseas convention on machine imaginative and prescient and development attractiveness on June 21 2012 in windfall, Rhode Island, united states. The papers discover using sleek laptop imaginative and prescient expertise in projects akin to automated segmentation and registration, localization of anatomical positive factors and detection of anomalies, in addition to 3D reconstruction and biophysical version personalization.

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Additional resources for Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging: Second International MICCAI Workshop, MCV 2012, Nice, France, October 5, 2012, Revised Selected Papers

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GSW only slightly outperforms an uniform weighting, which results in a simple max-voting scheme. We also compared our method to a recent group-wise registration approach (ABSORB [11]) a 3D implementation of which is publicly available. This method uses the atlas as a reference image, on which all test images are aligned iteratively to improve a mean image. Similarly to ours, this method also utilizes 34 T. Gass, G. Sz´ekely, and O. Goksel Table 1. 13 MR - corpus callosum CT - mandibles Fig. 3. Segmentation accuracy (Dice) of traditional single atlas based segmentation and our proposed method with LAW fusion for each atlas (x-axis) and target (y-axis) image combination.

Traditional atlas-based segmentation suffers from either a strong bias towards the selected atlas or the need for manual effort to create multiple atlas images. Similar to semi-supervised learning in computer vision, we study a method which exploits information contained in a set of unlabelled images by mutually registering them nonrigidly and propagating the single atlas segmentation over multiple such registration paths to each target. These multiple segmentation hypotheses are then fused by local weighting based on registration similarity.

Thus, a good solution is to keep the large assignments only for the good matches while suppress the distractions from ambiguous matches. To achieve this, we propose to (1) utilize the appearance-based line patch to exclude the in-correct matches when constructing the affinity matrix and (2) further apply sparsity to the assignment matrix during the optimization procedure to suppress the influence from ambiguous matches, as will be presented below. 5 0 (a) Assignment matrix X of SMAC (b) Assignment matrix X of our method 0 (c) The profiles along the pink lines Fig.

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