Book chapter
Mouse Brain Spatial Normalization: The Challenge of Sparse Data
Biomedical Image Registration, pp 349-357
2003
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A three-dimensional surface-based progressive alignment algorithm is proposed to recover nonlinear deformation field. The deformation field is represented with a multi-resolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. We report of its use in spatial normalization of mouse brains reconstructed from sectional material. The wavelet alignment algorithm produced more than threefold improvement in accuracy over an affine (linear) alignment. Its susceptibility to sparse sampling, a problem when the data is derived from tissue sections, was evaluated. Registration accuracy was reduced by only two fold as sampling decreased six- fold.
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Details
- Title
- Mouse Brain Spatial Normalization: The Challenge of Sparse Data
- Creators
- Smadar Gefen - Drexel UniversityOleh Tretiak - Drexel UniversityLouise Bertrand - Drexel UniversityJonathan Nissanov - Drexel University
- Publication Details
- Biomedical Image Registration, pp 349-357
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000187954800037
- Scopus ID
- 2-s2.0-0142214580
- Other Identifier
- 991019170389804721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Biomedical
- Imaging Science & Photographic Technology
- Radiology, Nuclear Medicine & Medical Imaging