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- DescriptionAutomatic 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, ise 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 des and prostate in major medical imaging modalities (CT, MRI, X-Ray and Ultrasound), demonstrating its efficiency and robustness.
- Author(s)Dorin Comaniciu,Yefeng Zheng
- PublisherSpringer-Verlag New York Inc.
- Date of Publication17/04/2014
- SubjectClinical Medicine: Professional
- Place of PublicationNew York
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Note64 black & white illustrations, 58 colour illustrations, 25 black & white tables, biography
- Weight596 g
- Width155 mm
- Height235 mm
- Spine20 mm
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