NDLI: Image Registration of Sectioned Brains

Content Provider	Springer Nature Link
Author	Schmitt, Oliver Modersitzki, Jan Heldmann, Stefan Wirtz, Stefan Fischer, Bernd
Copyright Year	2006
Abstract	The physical (microtomy), optical (microscopy), and radiologic (tomography) sectioning of biological objects and their digitization lead to stacks of images. Due to the sectioning process and disturbances, movement of objects during imaging for example, adjacent images of the image stack are not optimally aligned to each other. Such mismatches have to be corrected automatically by suitable registration methods.Here, a whole brain of a Sprague Dawley rat was serially sectioned and stained followed by digitizing the 20 μm thin histologic sections. We describe how to prepare the images for subsequent automatic intensity based registration. Different registration schemes are presented and their results compared to each other from an anatomical and mathematical perspective. In the first part we concentrate on rigid and affine linear methods and deal only with linear mismatches of the images. Digitized images of stained histologic sections often exhibit inhomogenities of the gray level distribution coming from staining and/or sectioning variations. Therefore, a method is developed that is robust with respect to inhomogenities and artifacts. Furthermore we combined this approach by minimizing a suitable distance measure for shear and rotation mismatches of foreground objects after applying the principal axes transform. As a consequence of our investigations, we must emphasize that the combination of a robust principal axes based registration in combination with optimizing translation, rotation and shearing errors gives rise to the best reconstruction results from the mathematical and anatomical view point.Because the sectioning process introduces nonlinear deformations to the relative thin histologic sections as well, an elastic registration has to be applied to correct these deformations.In the second part of the study a detailed description of the advances of an elastic registration after affine linear registration of the rat brain is given. We found quantitative evidence that affine linear registration is a suitable starting point for the alignment of histologic sections but elastic registration must be performed to improve significantly the registration result. A strategy is presented that enables to register elastically the affine linear preregistered~ rat brain sections and the first one hundred images of serial histologic sections through both occipital lobes of a human brain (6112 images). Additionally, we will describe how a parallel implementation of the elastic registration was realized. Finally, the computed force fields have been applied here for the first time to the morphometrized data of cells determined automatically by an image analytic framework.
Starting Page	5
Ending Page	39
Page Count	35
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	73
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-09-18
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	neuroimaging human and rat brain serial sections affine registration elastic registration matching alignment warping 3D-reconstruction Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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