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Shape Analysis in Medical Image Analysis, Shuo Li; Jo?o Manuel R. S. Tavares


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Цена: 15672.00р.
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Автор: Shuo Li; Jo?o Manuel R. S. Tavares
Название:  Shape Analysis in Medical Image Analysis
ISBN: 9783319375212
Издательство: Springer
Классификация:


ISBN-10: 3319375210
Обложка/Формат: Paperback
Страницы: 442
Вес: 0.63 кг.
Дата издания: 27.08.2016
Серия: Lecture Notes in Computational Vision and Biomechanics
Язык: English
Размер: 234 x 156 x 23
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: A landmark publication in its scope, this book features contributions by more than a dozen internationally renowned experts, addressing key topics in shape analysis in medical imaging, such as image segmentation, modeling, and classification techniques.


Medical Image Understanding and Analysis

Автор: Mar?a Vald?s Hern?ndez; V?ctor Gonz?lez-Castro
Название: Medical Image Understanding and Analysis
ISBN: 3319609637 ISBN-13(EAN): 9783319609638
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This book constitutes the refereed proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, held in Edinburgh, UK, in July 2017.
The 82 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on retinal imaging, ultrasound imaging, cardiovascular imaging, oncology imaging, mammography image analysis, image enhancement and alignment, modeling and segmentation of preclinical, body and histological imaging, feature detection and classification.
The chapters 'Model-Based Correction of Segmentation Errors in Digitised Histological Images' and 'Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering' are open access under a CC BY 4.0 license.

Natural User Interfaces in Medical Image Analysis

Автор: Marek R. Ogiela; Tomasz Hachaj
Название: Natural User Interfaces in Medical Image Analysis
ISBN: 3319077996 ISBN-13(EAN): 9783319077994
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This unique text/reference highlights a selection of practical applications of advanced image analysis methods for medical images. The text also presents significant problems related to new approaches and paradigms in image understanding and semantic image analysis.

Shape Analysis in Medical Image Analysis

Автор: Shuo Li; Jo?o Manuel R. S. Tavares
Название: Shape Analysis in Medical Image Analysis
ISBN: 3319038125 ISBN-13(EAN): 9783319038124
Издательство: Springer
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Цена: 16769.00 р.
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Описание: A landmark publication in its scope, this book features contributions by more than a dozen internationally renowned experts, addressing key topics in shape analysis in medical imaging, such as image segmentation, modeling, and classification techniques.

Guide to Medical Image Analysis

Автор: Toennies
Название: Guide to Medical Image Analysis
ISBN: 1447160967 ISBN-13(EAN): 9781447160960
Издательство: Springer
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Цена: 9781.00 р.
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Описание: Practical in approach, this book describes a range of common imaging techniques, reconstruction techniques and image artifacts, reviews image enhancement methods, discusses image transfer and archiving, and includes exercises and a glossary of abbreviations.

Spectral and Shape Analysis in Medical Imaging

Автор: Reuter
Название: Spectral and Shape Analysis in Medical Imaging
ISBN: 3319512366 ISBN-13(EAN): 9783319512365
Издательство: Springer
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Цена: 5870.00 р.
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Описание: This book constitutes the refereed post-conference proceedings of the First International Workshop on Spectral and Shape Analysis in Medical Imaging, SeSAMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016.The 10 submitted full papers presented in this volume were carefully reviewed.The papers reflect the following topics: spectral methods; longitudinal methods; and shape methods.

Cloud-Based Benchmarking of Medical Image Analysis

Автор: Allan Hanbury; Henning M?ller; Georg Langs
Название: Cloud-Based Benchmarking of Medical Image Analysis
ISBN: 3319496425 ISBN-13(EAN): 9783319496429
Издательство: Springer
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Цена: 6986.00 р.
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Описание: VISCERAL: Evaluation-as-a-Service for Medical Imaging.- Using the Cloud as a Platform for Evaluation and Data Preparation.- Ethical and Privacy Aspects of Using Medical Image Data.- Annotating Medical Image Data.- Datasets created in VISCERAL.- Evaluation Metrics for Medical Organ Segmentation and Lesion Detection.- VISCERAL Anatomy Benchmarks for Organ Segmentation and Landmark Localisation: Tasks and Results.- Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark.- Automatic Atlas-Free Multi-Organ Segmentation of Contrast-Enhanced CT Scans.- Multi-organ Segmentation Using Coherent Propagating Level Set Method Guided by Hierarchical Shape Priors and Local Phase Information.- Automatic Multi-organ Segmentation using Hierarchically-Registered Probabilistic Atlases.- Multi-Atlas Segmentation Using Robust Feature-Based Registration.- Combining Radiology Images and Clinical Meta-data for Multimodal Medical Case-based Retrieval.- Text and Content-based Medical Image Retrieval in the VISCERAL Retrieval Benchmark.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Автор: M. Jorge Cardoso; Tal Arbel; Gustavo Carneiro; Tan
Название: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
ISBN: 3319675575 ISBN-13(EAN): 9783319675572
Издательство: Springer
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Цена: 9083.00 р.
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Описание:

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017.

The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Fetal, Infant and Ophthalmic Medical Image Analysis

Автор: M. Jorge Cardoso; Tal Arbel; Andrew Melbourne; Hrv
Название: Fetal, Infant and Ophthalmic Medical Image Analysis
ISBN: 3319675605 ISBN-13(EAN): 9783319675602
Издательство: Springer
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Цена: 7685.00 р.
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Описание:

This book constitutes the refereed joint proceedings of the International Workshop on Fetal and Infant Image Analysis, FIFI 2017, and the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017.

The 8 full papers presented at FIFI 2017 and the 20 full papers presented at OMIA 2017 were carefully reviewed and selected. The FIFI papers feature research on advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period. The OMIA papers cover various topics in the field of ophthalmic image analysis.

Marginal Space Learning for Medical Image Analysis

Автор: Yefeng Zheng; Dorin Comaniciu
Название: Marginal Space Learning for Medical Image Analysis
ISBN: 1493955756 ISBN-13(EAN): 9781493955756
Издательство: Springer
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Цена: 11179.00 р.
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Описание: Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications.

Color Medical Image Analysis

Автор: M. Emre Celebi; Gerald Schaefer
Название: Color Medical Image Analysis
ISBN: 940178129X ISBN-13(EAN): 9789401781299
Издательство: Springer
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Цена: 16977.00 р.
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Описание: Recently, medical imaging modalities that use color information have been introduced to the formerly monochrome world of x-ray, CT and ultrasound. This book summarizes the state of the art in the utilization of color information in medical image analysis.


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