3 research outputs found
Registration of serial sections: An evaluation method based on distortions of the ground truths
Registration of histological serial sections is a challenging task. Serial
sections exhibit distortions and damage from sectioning. Missing information on
how the tissue looked before cutting makes a realistic validation of 2D
registrations extremely difficult.
This work proposes methods for ground-truth-based evaluation of
registrations. Firstly, we present a methodology to generate test data for
registrations. We distort an innately registered image stack in the manner
similar to the cutting distortion of serial sections. Test cases are generated
from existing 3D data sets, thus the ground truth is known. Secondly, our test
case generation premises evaluation of the registrations with known ground
truths. Our methodology for such an evaluation technique distinguishes this
work from other approaches. Both under- and over-registration become evident in
our evaluations. We also survey existing validation efforts.
We present a full-series evaluation across six different registration methods
applied to our distorted 3D data sets of animal lungs. Our distorted and ground
truth data sets are made publicly available.Comment: Supplemental data available under https://zenodo.org/record/428244
All-Pass Parametric Image Registration
Image registration is a required step in many practical applications that involve the acquisition of multiple related images. In this paper, we propose a methodology to deal with both the geometric and intensity transformations in the image registration problem. The main idea is to modify an accurate and fast elastic registration algorithm (Local All-PassLAP) so that it returns a parametric displacement field, and to estimate the intensity changes by fitting another parametric expression. Although we demonstrate the methodology using a low-order parametric model, our approach is highly flexible and easily allows substantially richer parametrisations, while requiring only limited extra computation cost. In addition, we propose two novel quantitative criteria to evaluate the accuracy of the alignment of two images (salience correlation) and the number of degrees of freedom (parsimony) of a displacement field, respectively. Experimental results on both synthetic and real images demonstrate the high accuracy and computational efficiency of our methodology. Furthermore, we demonstrate that the resulting displacement fields are more parsimonious than the ones obtained in other state-of-the-art image registration approaches