REGISTRATION OF ULTRA-HIGH RESOLUTION 3D PLI DATA OF HUMAN BRAINSECTIONS TO THEIR CORRESPONDING HIGH-RESOLUTION COUNTERPART
REGISTRATION OF ULTRA-HIGH RESOLUTION 3D PLI DATA OF HUMAN BRAINSECTIONS TO THEIR CORRESPONDING HIGH-RESOLUTION COUNTERPART
The structural analysis of nerve fibers of the human brain is animportant topic in current neuroscience. To obtain informationabout neural connections with micrometer resolution, polarizedlight imaging (3D PLI) of histological brain sectionsis well suited. In our application, both high-resolution (H...
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Personal Name(s): | Ali (Corresponding author) |
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Rohr / Axer, Markus / Amunts, Katrin / Eils / Wörz | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 |
Imprint: |
2017
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Physical Description: |
1-5 |
Conference: | IEEE International Symposium on Biomedical Imaging, Melbourne (Australia), 2017-04-18 - 2017-04-21 |
Document Type: |
Contribution to a conference proceedings |
Research Program: |
Supercomputing and Modelling for the Human Brain Human Brain Project Specific Grant Agreement 1 Connectivity and Activity |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
The structural analysis of nerve fibers of the human brain is animportant topic in current neuroscience. To obtain informationabout neural connections with micrometer resolution, polarizedlight imaging (3D PLI) of histological brain sectionsis well suited. In our application, both high-resolution (HR,64m in-plane pixel size) and ultra-high resolution (ultra-HR, 1.3 um) 3D PLI data of human brain sections are acquired.However, due to arbitrary translations and rotationscaused by the sectioning and mounting process, spatial coherencebetween sections is lost and image registration is necessary.We introduce a new feature-based approach for registrationof ultra-HR 3D PLI data to their corresponding HRimages. The approach is based on a novel multi-scale salientfeature detection method that is well suited for 3D PLI data.We have successfully evaluated the approach and applied it to83 sections of a human brain. An experimental comparisonwith previous state-of-the-art feature detectors demonstratesthe superior performance of our approach. |