Medical Image Recognition, Segmentation and Parsing (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
542
Utgivningsdatum
2015-12-02
Förlag
Academic Press
Illustrationer
illustrations
Dimensioner
236 x 196 x 33 mm
Vikt
1317 g
Antal komponenter
1
ISBN
9780128025819
Medical Image Recognition, Segmentation and Parsing (inbunden)

Medical Image Recognition, Segmentation and Parsing

Machine Learning and Multiple Object Approaches

Inbunden Engelska, 2015-12-02
1343
  • Skickas inom 10-15 vardagar.
  • Gratis frakt inom Sverige över 199 kr för privatpersoner.
Kan levereras innan julafton!
Finns även som
Visa alla 1 format & utgåvor
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.

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
Visa hela texten

Passar bra ihop

  1. Medical Image Recognition, Segmentation and Parsing
  2. +
  3. What If?

De som köpt den här boken har ofta också köpt What If? av Randall Munroe (häftad).

Köp båda 2 för 1481 kr

Kundrecensioner

Har du läst boken? Sätt ditt betyg »

Fler böcker av Kevin Zhou

  • Deep Learning for Medical Image Analysis

    Kevin Zhou

    Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts...

  • Handbook of Medical Image Computing and Computer Assisted Intervention

    Kevin Zhou

    Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current ...

Övrig information

S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).

Innehållsförteckning

Preface Chapter 1 Introduction to Medical Image Recognition and Parsing Chapter 2 Discriminative Anatomy Detection: Classification vs. Regression Chapter 3: Information Theoretic Landmark Detection Chapter 4: Submodular Landmark Detection Chapter 5: Random Forests for Anatomy Recognition Chapter 6: Integrated Detection Network for Multiple Object Recognition Chapter 7: Optimal Graph-Based Method for Multi-Object Segmentation Chapter 8: Parsing of Multiple Organs Using Learning Method and Level Sets Chapter 9: Context Integration for Rapid Multiple Organ Parsing Chapter 10: Multi-Atlas Methods and Label Fusion Chapter 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 Bones Chapter 19: Lymph Node Segmentation Chapter 20: Polyp Segmentation from CT Colonoscopy