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Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2005 ; 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part II
Robotics, Image-Guided Surgery and Interventions -- Image Registration II -- Medical Image Computing -- Atlases -- Shape I -- Structural and Functional Brain Analysis -- Model-Based Image Analysis -- Image-Guided Intervention: Simulation, Modeling and Display -- Simulation and Modeling II -- Medical Image Computing -- Shape II -- Image Segmentation and Analysis II -- Image Registration III --
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
Data Mining and Bioinformatics ; 1st International Workshop, VDMB 2006, Seoul, Korea, September 11, 2006, Revised Selected Papers
This volume contains the papers presented at the inaugural workshop on Data Mining and Bioinformatics at the 32nd International Conference on Very Large Data Bases (VLDB). The purpose of this workshop was to begin bringing - gether researchersfrom database, data mining, and bioinformatics areas to help leverage respective successes in each to the others.
Biomedical Image Registration ; 3rd International Workshop, WBIR 2006, Utrecht, The Netherlands, July 9-11, 2006, Proceedings
This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Biomedical Image Registration. The 20 revised full papers and 18 revised poster papers presented were carefully reviewed and selected for inclusion in the book. The papers cover all areas of biomedical image registration; methods of registration, biomedical applications, and validation of registration.
Advances in bioinformatics and computational biology ; 1st Brazilian symposium on bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings
This book constitutes the refereed proceedings of the Brazilian Symposium on Bioinformatics, BSB 2005, The 15 revised full papers and 10 revised extended abstracts presented together with 3 invited papers were carefully reviewed and selected from 55 submissions. The papers address a broad range of current topics in computational biology and bioinformatics.
Advanced data mining and applications ; Vol. 3584 ; 1st International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005, Proceedings
This book constitutes the refereed proceedings of the First International Conference on Advanced Data Mining and Applications, ADMA 2005, held in Wuhan, China in July 2005. The 25 revised full papers and 75 revised short papers presented were carefully peer-reviewed and The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
