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

Dorin Comaniciu

ISBN 10: 1493905996 / ISBN 13: 9781493905997

Published by Springer New York, Springer US, 2014

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781493905997

Bibliographic Details

• Title: Marginal Space Learning for Medical Image Analysis : Efficient Detection and Segmentation of Anatomical Structures
• Publisher: Springer New York, Springer US
• Publication Date: 2014
• Language: English
• ISBN 10: 1493905996
• ISBN 13: 9781493905997
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 22D millimeters 241 millimeters width by 160 millimeters height by 22 millimeters depth
• Weight: 600 grams

About this title

Synopsis

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.

Review

“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)