Automated registration method with disparity detection for remotely sensed multispectral images

1996 ◽  
Author(s):  
Hiroshi Hanaizumi ◽  
Yutaka Kanemoto ◽  
Sadao Fujimura
Keyword(s):  
Remotely Sensed ◽  
Multispectral Images ◽  
Registration Method ◽  
Disparity Detection ◽  
Automated Registration

scholarly journals Temporal change enhancement in multispectral images remotely sensed from satellites

1997 ◽  
Author(s):  
Bill P. Pfaff ◽  
Doran Baker ◽  
Lloyd G. Allred ◽  
Gene Ware
Keyword(s):  
Temporal Change ◽  
Remotely Sensed ◽  
Multispectral Images

scholarly journals Tensor Image Registration Library: Automated Non-Linear Registration of Sparsely Sampled Histological Specimens to Post-Mortem MRI of the Whole Human Brain

2019 ◽  
Author(s):  
Istvan N. Huszar ◽  
Menuka Pallebage-Gamarallage ◽  
Sean Foxley ◽  
Benjamin C. Tendler ◽  
Anna Leonte ◽  
...  
Keyword(s):  
Image Registration ◽  
Human Brain ◽  
Brain Mri ◽  
List Type ◽  
Post Mortem ◽  
Whole Brain ◽  
Registration Method ◽  
Volume Registration ◽  
Histological Images ◽  
Automated Registration

AbstractThere is a need to understand the histopathological basis of MRI signal characteristics in complex biological matter. Microstructural imaging holds promise for sensitive and specific indicators of the early stages of human neurodegeneration but requires validation against traditional histological markers before it can be reliably applied in the clinical setting. Validation relies on a precise and preferably automatic method to align MRI and histological images of the same tissue, which poses unique challenges compared to more conventional MRI-to-MRI registration.A customisable open-source platform, Tensor Image Registration Library (TIRL) is presented. Based on TIRL, a fully automated pipeline was implemented to align small stained histological images with dissection photographs of corresponding tissue blocks and coronal brain slices, and further with high-resolution (0.5 mm) whole-brain post-mortem MRI data. The pipeline performed three separate deformable registrations to achieve accurate mapping between whole-brain MRI and small-slide histology coordinates. The robustness and accuracy of the individual registration steps were evaluated using both simulated data and real-life images from 6 different anatomical locations of one post-mortem human brain.The automated registration method demonstrated sub-millimetre accuracy in all steps, robustness against tissue damage, and good reproducibility between experiments. The method also outperformed manual landmark-based slice-to-volume registration, also correcting for curvatures in the slicing plane. Due to the customisability of TIRL, the pipeline can be conveniently adapted for other research needs and is therefore suitable for the large-scale comparison of routinely collected histology and MRI data.HighlightsTIRL: new framework for prototyping bespoke image registration pipelinesPipeline for automated registration of small-slide histology to whole-brain MRISlice-to-volume registration accounting for through-plane deformationsNo need for serial histological sampling

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