Image Registration of Sectioned Brains

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 6 Schmitt et al.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.

Biodegradation of porous calcium phosphate scaffolds in an ectopic bone formation model studied by X-ray computed microtomograph

“…Such techniques have for instance been applied to the evaluation of bone loss in ovariectomized animal models (Waarsing et al, 2006;Klinck et al, 2008). At the present, there are a number of different kinds of registration techniques (Brown 1992;van den Elsen et al, 1993;Modersitzki, 2004;Schmitt et al, 2007) for different registration problems, in particular for the alignment of images of serial sections. This includes e.g., rigid, affine and elastic registration.…”

Elastic volume reconstruction from series of ultra-thin microscopy sections

“…Similar elastic constraints have been proposed earlier 9,10 , combining search for an elastic alignment and a pixel-based pairwise similarity estimate between adjacent sections in an iterative solution. They propose initial linear pre-alignment of the section series based on variants of principal component analysis.…”

Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors

“…Large scale image registration has many applications in both biomedical research [10,22,32] and geophysics [33]. However, there are currently few works addressing image registration algorithms intended to run efficiently on high performance computing environments.…”

“…The work on parallel image registration on multicomputers is limited [22] and is restricted to either large computer clusters [34][35][36] or IBM cell clusters [37]. Clusters of GPUs have been used to implement other heavy workload tasks [38], mostly within the simulation and visualization fields.…”