Marginal Space Learning for Medical Image Analysis

Efficient Detection and Segmentation of Anatomical Structures

  • Yefeng Zheng
  • Dorin Comaniciu

Table of contents

  1. Front Matter
    Pages i-xx
  2. Yefeng Zheng, Dorin Comaniciu
    Pages 1-23
  3. Yefeng Zheng, Dorin Comaniciu
    Pages 25-65
  4. Yefeng Zheng, Dorin Comaniciu
    Pages 79-101
  5. Yefeng Zheng, Dorin Comaniciu
    Pages 103-135
  6. Yefeng Zheng, Dorin Comaniciu
    Pages 137-158
  7. Yefeng Zheng, Dorin Comaniciu
    Pages 199-256
  8. Yefeng Zheng, Dorin Comaniciu
    Pages 257-261
  9. Back Matter
    Pages 263-268

About this book

Introduction

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. This book presents an efficient object detection and segmentation framework, called Marginal Space Learning, which runs at a sub-second speed on a current desktop computer, faster than the state-of-the-art. Trained with a sufficient number of data sets, Marginal Space Learning is also robust under imaging artifacts, noise and anatomical variations. The book showcases 35 clinical applications of Marginal Space Learning and its extensions to detecting and segmenting various anatomical structures, such as the heart, liver, lymph nodes and prostate in major medical imaging modalities (CT, MRI, X-Ray and Ultrasound), demonstrating its efficiency and robustness.

Keywords

3D medical image data Anatomical structure detection artificial intelligence computed tomography human body parsing human organ pose estimation intelligent image analysis system machine learning magnetic resonance imaging marginal space learning medical image analysis medical image segmentation medical imaging object detection organ segmentation ultrasound

Authors and affiliations

  • Yefeng Zheng
    • 1
  • Dorin Comaniciu
    • 2
  1. 1.Imaging and Computer VisionSiemens Corporate TechnologyPrincetonUSA
  2. 2.Imaging and Computer VisionSiemens Corporate TechnologyPrincetonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-0600-0
  • Copyright Information Springer Science+Business Media New York 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4939-0599-7
  • Online ISBN 978-1-4939-0600-0
  • About this book
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