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Medical Image Computing and Computer-Assisted Intervention - MICCAI'99: Second International Conference, Cambridge, UK, September 19-22, 1999, Proceedings: 1679 (Lecture Notes in Computer Science) - Softcover
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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.
Reseña del editor
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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- VerlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. K
- Erscheinungsdatum1999
- ISBN 10 354066503X
- ISBN 13 9783540665038
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten1242
- HerausgeberTaylor Chris, Colchester Alan
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Medical Image Computing and Computer-Assisted Intervention - MICCAI'99: Second International Conference, Cambridge, UK, September 19-22, 1999, Proceedings (Lecture Notes in Computer Science)
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9783540665038
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Medical Image Computing and Computer-Assisted Intervention - MICCAI 99
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 I. Bestandsnummer des Verkäufers 4897733
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Medical Image Computing and Computer-Assisted Intervention - MICCAI'99
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Springer Berlin Heidelberg Sep 1999, 1999
ISBN 10: 354066503X
ISBN 13: 9783540665038
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 1288 pp. Englisch. Bestandsnummer des Verkäufers 9783540665038
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