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Marginal Space Learning for Medical Image Analysis: Efficient Detection and Segmentation of Anatomical Structures by Yefeng Zheng at Abbey's Bookshop,
Marginal Space Learning for Medical Image Analysis: Efficient Detection and Segmentation of Anatomical...
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Yefeng Zheng Dorin Comaniciu

Marginal Space Learning for Medical Image Analysis

Efficient Detection and Segmentation of Anatomical Structures

Yefeng Zheng,  Dorin Comaniciu

$130.95   $105.07

Hardback

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English
Springer-Verlag New York Inc.
17 April 2014
Medical imaging; Computer vision
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.
By:   ,
Imprint:   Springer-Verlag New York Inc.
Country of Publication:   United States
Edition:   2014 ed.
Dimensions:   Height: 235mm,  Width: 155mm,  Spine: 20mm
Weight:   5.561kg
ISBN:   9781493905997
ISBN 10:   1493905996
Pages:   268
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

Reviews for Marginal Space Learning for Medical Image Analysis: Efficient Detection and Segmentation of Anatomical Structures

“This book presents a generic learning-based method for efficient 3D object detection called marginal space learning (MSL). … Each chapter ends with a remarkable bibliography on the topics covered. This book is suited for students and researchers with interest in medical image analysis.” (Oscar Bustos, zbMATH 1362.92004, 2017)


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