Marginal Space Learning for Medical Image Analysis

Dorin Comaniciu

ISBN 10: 1493905996 / ISBN 13: 9781493905997

Editore: Springer New York, Springer US Apr 2014, 2014

Riguardo questo articolo

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch.

Codice articolo 9781493905997

Dati bibliografici

• Titolo: Marginal Space Learning for Medical Image Analysis
• Editore: Springer New York, Springer US Apr 2014
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1493905996
• ISBN 13: 9781493905997
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 22 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 22 millimetri di profondità
• Peso: 600 grammi

Descrizione articolo

Riassunto

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.

Recensione

“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)