Chris Taylor / Alan Colchester (eds.)

Medical Image Computing and Computer-Assisted Intervention - MICCAI'99

Second International Conference, Cambridge, UK, September 19-22, 1999, Proceedings
Herausgegeben:Taylor, Chris; Colchester, Alan

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'99, held in Cambridge, UK, in September 1999.The 133 revised full papers presented were carefully reviewed and selected from a total of 213 full-length papers submitted. The book is divided into topical sections on data-driven segmentation, segmentation using structural models, image processing and feature detection, surfaces and shape, measurement and interpretation, spatiotemporal and diffusion tensor analysis, registration and fusion, visualization, image-guided intervention, robotic systems, and biomechanics and simulation.

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Abnormal white matter of the brain is common to patients with one of s- eral di?erent diseases (including multiple sclerosis (MS) and Alzheimer s d- ease (AD)) and also appears in normal (asymptomatic) aging (NA) subjects. Better characterization of the nature of these white matter changes can help to improve our understanding of the biological processes at work. Clinically, it is interesting to be able to di?erentiate between di?erent disease states and to ?nd markerswhich allowearlydiagnosis.Conventionalspin echo magnetic resonance imaging is sensitive to these white matter changes. MRI studies of patients and volunteershaveindicatedthatthepatternsofbrainchangeassociatedwiththese processes are di?erent. An important goal is to be able to quantitatively study these di?erences. Many automated and semi-automated segmentation algorithms for quan- tatively assessing these brain changes have been developed and validated. Most of these algorithms have aimed at determining a binary characterization of each voxelas one of a groupof possible tissue classes.This approachhas been limited bytwofactors.First,abnormalwhitematter isoftenisointensewithnormalgrey matter and previous studies have been limited by the inability to discriminate between some abnormal white matter and normal grey matter [1,2]. Secondly, white matter damage appears as an heterogenous region of abnormal signal - tensity but binarization of the segmentation treats all levels of signal intensity abnormality equally.

Produktdetails

• Lecture Notes in Computer Science 1679
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-66503-8
• 1999
• Seitenzahl: 1288
• Erscheinungstermin: 8. September 1999
• Englisch
• Abmessung: 235mm x 155mm x 69mm
• Gewicht: 1546g
• ISBN-13: 9783540665038
• ISBN-10: 354066503X
• Artikelnr.: 09256858

Inhaltsangabe

Data-Driven Segmentation.- Segmentation of Meningiomas and Low Grade Gliomas in MRI.- Automated Segmentation of MS Lesions from Multi-channel MR Images.- Measurement of Infarct Volume in Stroke Patients Using Adaptive Segmentation of Diffusion Weighted MR Images.- Quantitative Modelling of Microcalcification Detection in Digital Mammography.- Interactive Direct Volume Rendering of Dural Arteriovenous Fistulae in MR-CISS Data.- Segmentation of White Matter Lesions from Volumetric MR Images.- Fractional Segmentation of White Matter.- A Modified Fuzzy C-Means Algorithm for MRI Bias Field Estimation and Adaptive Segmentation.- Statistical 3D Vessel Segmentation Using a Rician Distribution.- Retinal Blood Vessel Segmentation by Means of Scale-Space Analysis and Region Growing.- Liver Blood Vessels Extraction by a 3-D Topological Approach.- Segmentation Using Structural Models.- Tamed Snake: A Particle System for Robust Semi-automatic Segmentation.- Interactive Medical Image Segmentation with United Snakes.- Active Shape Model-Based Segmentation of Digital X-ray Images.- Nonrigid 3-D/2-D Registration of Images Using Statistical Models.- A New Approach to 3D Sulcal Ribbon Finding from MR Images.- Automated Segmentation of Sulcal Regions.- Cylindrical Echocardiographic Image Segmentation Based on 3D Deformable Models.- Active Model Based Carotid Ultrasonic Data Segmentation.- Automatic Segmentation of Lung Fields in Chest Radiographs.- Automatic Reconstruction of 3D Geometry Using Projections and a Geometric Prior Model.- 3D Image Matching Using a Finite Element Based Elastic Deformation Model.- Image Processing and Feature Detection.- Quantitative Comparison of Sinc-Approximating Kernels for Medical Image Interpolation.- A Post-processing Technique to Suppress Fluid Signal and Increase Contrast in Multispectral MR Exams of MS Patients.- De-noising h int Surfaces: A Physics-Based Approach.- ERS Transform for the Detection of Bronchi on CT of the Lungs.- Detection of Pulmonary Nodules on CT and Volumetric Assessment of Change over Time.- Improving the Detection Performance in Semi-automatic Landmark Extraction.- Automatic Classification of Linear Structures in Mammographic Images.- Surfaces and Shape.- Conformal Geometry and Brain Flattening.- Quasi-Conformally Flat Mapping the Human Cerebellum.- Rendering the Unfolded Cerebral Cortex.- Tessellated Surface Reconstruction from 2D Contours.- Accurate Robust Symmetry Estimation.- Global Shape from Shading for an Endoscope Image.- Measurement and Interpretation.- The Measurement of Focal Diurnal Variation in the Femoral Articular Cartilage of the Knee.- Three-Dimensional Reconstruction and Quantification of Hip Joint Cartilages from Magnetic Resonance Images.- Quantification of Cerebral Grey and White Matter Asymmetry from MRI.- Quantitation of Vessel Morphology from 3D MRA.- A Patient-Specific Computer Model for Prediction of Clinical Outcomes in the Cerebral Circulation Using MR Flow Measurements.- Exploratory Factor Analysis in Morphometry.- Potential Usefulness of Curvature Based Description for Differential Diagnosis of Pulmonary Nodules.- Pulmonary Organs Analysis Method and Its Evaluation Based on Thoracic Thin-Section CT Images.- An Automatic Approach for 3-D Facial Shape Change Analysis by Combination of ASM and Morphometric Tools.- Spatiotemporal and Diffusion Tensor Analysis.- Segmentation of Echocardiographic Image Sequences Using Spatio-temporal Information.- 3D Cardiac Deformation from Ultrasound Images.- Directional Representations of 4D Echocardiography for Temporal Quantification of LV Volume.- Image Processing for Diffusion Tensor Magnetic Resonance Imaging.- Inferring the Brain Connectivity from MR Diffusion Tensor Data.- Strategies for Data Reorientation during Non-rigid Warps of Diffusion Tensor Images.- Analysis of Functional MRI Data Using Mutual Information.- Statistical Segmentation of fMRI Activations Using Contextual Clustering.- Using Sulcal Basins for Analyzin