Computer Vision Approaches to Medical Image Analysis [electronic resource] : Second International ECCV Workshop, CVAMIA 2006, Graz, Austria, May 12, 2006, Revised Papers /
Workshop on Computer Vision Approaches to Medical Image AnalysisCorporate Author
Workshop on Computer Vision Approaches to Medical Image Analysis(2nd :2006 :Graz, Austria)
Workshop on Computer Vision Approaches to Medical Image Analysis
Beichel, Reinhard R.editor.
Sonka, Milan.editor.
Medical imaging and medical image analysis are developing rapidly. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. This was the second time that a satellite workshop,solely devoted to medical image analysis issues, was held in conjunction with the European Conference on Computer Vision (ECCV), and we are optimistic that this will become a tradition at ECCV. We received 38 full-length paper submissions to the second Computer Vision Approaches to Medical Image Analysis (CVAMIA) Workshop, out of which 10 were accepted for oral and 11 for poster presentation after a rigorous peer-review process. In addition, the workshop included three invited talks. The ?rst was given by Maryellen Giger from the University of Chicago, USA — titled “Multi-Modality Breast CADx”.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Clinical Applications -- Melanoma Recognition Using Representative and Discriminative Kernel Classifiers -- Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis -- Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI -- Accurate Measurement of Cartilage Morphology Using a 3D Laser Scanner -- Image Registration -- Quantification of Growth and Motion Using Non-rigid Registration -- Image Registration Accuracy Estimation Without Ground Truth Using Bootstrap -- SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images -- Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization -- Image Segmentation and Analysis -- Comparative Analysis of Kernel Methods for Statistical Shape Learning -- Segmentation of Dynamic Emission Tomography Data in Projection Space -- A Framework for Unsupervised Segmentation of Multi-modal Medical Images -- Poster Session -- An Integrated Algorithm for MRI Brain Images Segmentation -- Spatial Intensity Correction of Fluorescent Confocal Laser Scanning Microscope Images -- Quasi-conformal Flat Representation of Triangulated Surfaces for Computerized Tomography -- Bony Structure Suppression in Chest Radiographs -- A Minimally-Interactive Watershed Algorithm Designed for Efficient CTA Bone Removal -- Automatic Reconstruction of Dendrite Morphology from Optical Section Stacks -- Modeling the Activity Pattern of the Constellation of Cardiac Chambers in Echocardiogram Videos -- A Study on the Influence of Image Dynamics and Noise on the JPEG 2000 Compression Performance for Medical Images -- Fast Segmentation of the Mitral Valve Leaflet in Echocardiography -- Three Dimensional Tissue Classifications in MR Brain Images -- 3-D Ultrasound Probe Calibration for Computer-Guided Diagnosis and Therapy.
Medical imaging and medical image analysis are developing rapidly. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. This was the second time that a satellite workshop,solely devoted to medical image analysis issues, was held in conjunction with the European Conference on Computer Vision (ECCV), and we are optimistic that this will become a tradition at ECCV. We received 38 full-length paper submissions to the second Computer Vision Approaches to Medical Image Analysis (CVAMIA) Workshop, out of which 10 were accepted for oral and 11 for poster presentation after a rigorous peer-review process. In addition, the workshop included three invited talks. The ?rst was given by Maryellen Giger from the University of Chicago, USA — titled “Multi-Modality Breast CADx”.
Optical data processing.
Artificial intelligence.
Pattern recognition.
Computer graphics.
Health informatics.
Bioinformatics.
Image Processing and Computer Vision.
Artificial Intelligence.
Pattern Recognition.
Computer Graphics.
Health Informatics.
Bioinformatics.

Lecture Notes in Computer Science