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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.
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Statistical Methods in Video Processing - ECCV 2004 Workshop SMVP 2004, Prague, Czech Republic, May 16, 2004, Revised Selected Papers (Paperback, 2004 ed.)
Dorin Comaniciu, Kenichi Kanatani, Rudolf Mester, David Suter
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R1,600
Discovery Miles 16 000
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Ships in 10 - 15 working days
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The 2nd International Workshop on Statistical Methods in Video
Processing, SMVP 2004, was held in Prague, Czech Republic, as an
associated workshop of ECCV 2004, the 8th European Conference on
Computer Vision. A total of 30 papers were submitted to the
workshop. Of these, 17 papers were accepted for presentation and
included in these proceedings, following a double-blind review
process. The workshop had 42 registered participants. The focus of
the meeting was on recent progress in the application of - vanced
statistical methods to solve computer vision tasks. The one-day
scienti?c program covered areas of high interest in vision
research, such as dense rec- struction of 3D scenes, multibody
motion segmentation, 3D shape inference, errors-in-variables
estimation, probabilistic tracking, information fusion, optical
?owcomputation, learningfornonstationaryvideodata,
noveltydetectionin- namic backgrounds, background modeling,
grouping using feature uncertainty, and crowd segmentation from
video. We wish to thank the authors of all submitted papers for
their interest in the
workshop.Wealsowishtothankthemembersofourprogramcommitteeandthe
external reviewers for their commitment of time and e?ort in
providing valuable recommendations for each submission. We are
thankful to Vaclav Hlavac, the General Chair of ECCV 2004, and to
Radim Sara, for the local organization of the workshop and
registration management. We hope you will ?nd these proceedings
both inspiring and of high scienti?c qualit
Artificial Intelligence for Computational Modeling of the Heart
presents recent research developments towards streamlined and
automatic estimation of the digital twin of a patient's heart by
combining computational modeling of heart physiology and artificial
intelligence. The book first introduces the major aspects of
multi-scale modeling of the heart, along with the compromises
needed to achieve subject-specific simulations. Reader will then
learn how AI technologies can unlock robust estimations of cardiac
anatomy, obtain meta-models for real-time biophysical computations,
and estimate model parameters from routine clinical data. Concepts
are all illustrated through concrete clinical applications.
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