Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008; 11th International C Dimitris Metaxas,Leon Axel,Gábor Székely Con

猛然一拉

Gum-Disease

lethal

Belief Propagation for Depth Cue Fusion in Minimally Invasive Surgeryosterior node probabilities in a node’s Markov blanket together with the monocular and stereo depth maps. Detailed phantom validation and in vivo results are provided to demonstrate the accuracy, robustness, and practical value of the technique.

預感

Impact of Rician Adapted Non-Local Means Filtering on HARDIweighted (DW) images and averaging these images along each direction can be avoided because NLMr filtering of the individual DW images produces better quality generalized fractional anisotropy maps and more accurate ODF fields than when computed from the averaged DW datasets.

CEDE

Towards Regional Elastography of Intracranial Aneurysmsoise perturbation (<5mm) is applied to the reference deformations and strains, showing that the robustness of the clustering decreases to 75% and errors of the stiffness estimates remain below 10% of the reference values.

被告

Muscularis

Volumetric Ultrasound Panorama Based on 3D SIFTn and matching. Our method has been validated on both phantom and clinical data sets of human liver. Experimental results show the effectiveness and stability of our approach, and the precision of our method is comparable to that of the position tracker based registration.

cravat

cochlea

Motion Correction in Respiratory Gated Cardiac PET/CT Using Multi-scale Optical Flowlow based correction of the PET data is proposed. This includes deformation of the CT data for accurate attenuation and listmode based reconstruction. All methods are applied to real patient data and are evaluated with respect to three criteria.

整理

Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinopletely automated image analysis pipeline to predict the survival of RCC patients based on the analysis of immunohistochemical staining of MIB-1 on tissue microarrays. A random forest classifier detects cell nuclei of cancerous cells and predicts their staining. The classifier training is achieved b