Marginal space learning for medical image analysis :
General Material Designation
[Book]
Other Title Information
efficient detection and segmentation of anatomical structures /
First Statement of Responsibility
Yefeng Zheng, Dorin Comaniciu
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource :
Other Physical Details
illustrations (some color)
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index
CONTENTS NOTE
Text of Note
Marginal Space Learning -- Comparison of Marginal Space Learning and Full Space Learning in 2D -- Constrained Marginal Space Learning -- Part-Based Object Detection and Segmentation -- Optimal Mean Shape for Nonrigid Object Detection and Segmentation -- Nonrigid Object Segmentation: Application to Four-Chamber Heart Segmentation -- Applications of Marginal Space Learning in Medical Imaging -- Conclusions and Future Work
0
SUMMARY OR ABSTRACT
Text of Note
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
OTHER EDITION IN ANOTHER MEDIUM
Title
Marginal Space Learning for Medical Image Analysis
International Standard Book Number
9781493905997
TOPICAL NAME USED AS SUBJECT
Diagnostic imaging.
Image analysis.
Artificial Intelligence (incl. Robotics)
Computer Imaging, Vision, Pattern Recognition and Graphics.