Marginal Space Learning for Medical Image Analysis
Efficient Detection and Segmentation of Anatomical Structures
Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Health & Well Being, Medical, Medical Science, Biochemistry, General Computing
| Author: | Dorin Comaniciu, Yefeng Zheng | ISBN: | 9781493906000 |
| Publisher: | Springer New York | Publication: | April 16, 2014 |
| Imprint: | Springer | Language: | English |
| Author: | Dorin Comaniciu, Yefeng Zheng |
| ISBN: | 9781493906000 |
| Publisher: | Springer New York |
| Publication: | April 16, 2014 |
| Imprint: | Springer |
| Language: | English |
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.
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.
