Bibliothèque de L'IMIST/CNRST

Workshop Overview -- Overview of the Third Workshop on Medical Content–Based Retrieval for Clinical Decision Support (MCBR–CDS 2012) -- Invited Talk -- A Polynomial Model of Surgical Gestures for Real-Time Retrieval of Surgery Videos -- Methods -- Exploiting 3D Part-Based Analysis, Description and Indexing to Support Medical Applications -- Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models -- 3D/4D Data Retrieval -- Retrieval of 4D Dual Energy CT for Pulmonary Embolism Diagnosis -- Immediate ROI Search for 3-D Medical Images -- The Synergy of 3D SIFT and Sparse Codes for Classification of Viewpoints from Echocardiogram Videos -- Assessing the Classification of Liver Focal Lesions by Using Multi-phase Computer Tomography Scans -- Invited Talk -- VISCERAL: Towards Large Data in Medical Imaging — Challenges and Directions -- Visual Features -- Customised Frequency Pre-filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images -- Bag–of–Colors for Biomedical Document Image Classification -- Multimodal Retrieval -- An SVD–Bypass Latent Semantic Analysis for Image Retrieval -- Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes.

This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2012, held in Nice, France, in October 2012. The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. The papers are divided on several topics on image analysis of visual or multimodal medical data (X-ray, MRI, CT, echo videos, time series data), machine learning of disease correlations in visual or multimodal data, algorithms for indexing and retrieval of data from visual or multimodal medical databases, disease model-building and clinical decision support systems based on visual or multimodal analysis, algorithms for medical image retrieval or classification, systems of retrieval or classification using the ImageCLEF collection.