Marginal Space Learning for Medical Image Analysis - Efficient Detection and Segmentation of Anatomical Structures (Hardcover, 2014 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	April 2014
First published:	2014
Authors:	Yefeng Zheng • Dorin Comaniciu
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Hardcover
Pages:	268
Edition:	2014 ed.
ISBN-13:	978-1-4939-0599-7
Categories:	Books > Medicine > Other branches of medicine > Medical imaging > General Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Promotions
LSN:	1-4939-0599-6
Barcode:	9781493905997

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