الصفحة 1
الصفحة 1
Medical Imaging and Augmented Reality ; 4th International Workshop Tokyo, Japan, August 1-2, 2008 Proceedings
Constitutes the refereed proceedings of the 4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008, held in Tokyo, Japan, in August 2008.The 44 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 90 submissions. The papers are organized in topical sections on surgical planning and simulation, medical image computing, image analysis, shape modeling and morphometry, image-guided robotics, image-guided intervention, interventional imaging, image registration, augmented reality, and image segmentation.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007; 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part II
The 10th International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI2007, washeldattheBrisbaneConventionandExhibition Centre, South Bank, Brisbane, Australia from 29th October to 2nd November 2007. MICCAI has become a premier international conference in this domain, with in-depth papers on the multidisciplinary ?elds of biomedical image computing, computer assisted intervention and medical robotics.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005 ; 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I
This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using highdimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a crossvalidation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size
Intelligent robotics and applications ; 1st International Conference, ICIRA 2008, Wuhan, China, October 15-17, 2008, Proceedings, Part I
These two volumes constitute the refereed proceedings of the First International Conference on Intelligent Robotics and Applications, ICIRA 2008, held in Wuhan, China, in October 2008.The 265 revised full papers presented were thoroughly reviewed and selected from 552 submissions; they are devoted but not limited to robot motion planning and manipulation; robot control; cognitive robotics; rehabilitation robotics; health care and artificial limb; robot learning; robot vision; human-machine interaction & coordination; mobile robotics.
Intelligent robotics and applications ; 1st International Conference, ICIRA 2008 Wuhan, China, October 15-17, 2008 Proceedings, Part II
This two volumes constitute the refereed proceedings of the First International Conference on Intelligent Robotics and Applications, ICIRA 2008, held in Wuhan, China, in October 2008.The 265 revised full papers presented were thoroughly reviewed and selected from 552 submissions; they are devoted but not limited to robot motion planning and manipulation; robot control; cognitive robotics; rehabilitation robotics; health care and artificial limb; robot learning; robot vision; human-machine interaction & coordination; mobile robotics.
