Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches: The MICCAI Society book Series
Autor S. Kevin Zhouen Limba Engleză Hardback – dec 2015
Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.
Learn:
- Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects
- Methods and theories for medical image recognition, segmentation and parsing of multiple objects
- Efficient and effective machine learning solutions based on big datasets
- Selected applications of medical image parsing using proven algorithms
- Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects
- Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets
- Includes algorithms for recognizing and parsing of known anatomies for practical applications
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Specificații
ISBN-13: 9780128025819
ISBN-10: 0128025816
Pagini: 542
Dimensiuni: 191 x 235 x 33 mm
Greutate: 1.32 kg
Editura: ELSEVIER SCIENCE
Seria The MICCAI Society book Series
ISBN-10: 0128025816
Pagini: 542
Dimensiuni: 191 x 235 x 33 mm
Greutate: 1.32 kg
Editura: ELSEVIER SCIENCE
Seria The MICCAI Society book Series
Public țintă
Industry practitioners and university researchers in medical imaging.Cuprins
PrefaceChapter 1 Introduction to Medical Image Recognition and ParsingChapter 2 Discriminative Anatomy Detection: Classification vs. RegressionChapter 3: Information Theoretic Landmark DetectionChapter 4: Submodular Landmark DetectionChapter 5: Random Forests for Anatomy Recognition Chapter 6: Integrated Detection Network for Multiple Object RecognitionChapter 7: Optimal Graph-Based Method for Multi-Object Segmentation Chapter 8: Parsing of Multiple Organs Using Learning Method and Level SetsChapter 9: Context Integration for Rapid Multiple Organ ParsingChapter 10: Multi-Atlas Methods and Label FusionChapter 11: Multi-Compartment Segmentation Framework Chapter 12: Deformable Segmentation via Sparse Representation and Dictionary Learning Chapter 13: Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection Chapter 14: Whole Brain Anatomical Structure Parsing Chapter 15: Aortic and Mitral Valve Segmentation Chapter 16: Parsing of Heart, Chambers and Coronary Vessels Chapter 17: Spine Segmentation Chapter 18: Parsing of Rib and Knee BonesChapter 19: Lymph Node Segmentation Chapter 20: Polyp Segmentation from CT Colonoscopy