Marginal space learning for medical image analysis [electronic resource] : efficient detection and segmentation of anatomical structures / Yefeng Zheng, Dorin Comaniciu.

Marginal space learning for medical image analysis [electronic resource] : efficient detection and segmentation of anatomical structures / Yefeng Zheng, Dorin Comaniciu.

9781493905997
1493905996
9781493906000 electronic book
1493906003 electronic book

New York : Springer, [2014]

©2014

English

1 online resource : illustrations (some color)

10.1007/978-1-4939-0600-0 doi

RC78.7.D53

616.0757

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.

Includes bibliographical references and index.

Access limited to authorized users.

Description based on print version record.

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