Algorithms for accurate 3D registration of neuronal images acquired by confocal scanning laser microscopy

Abstract

SummaryThis paper presents automated and accurate algorithms based on high-order transformation models for registering threedimensional (3D) confocal images of dye-injected neurons. The algorithms improve upon prior methods in several ways, and meet the more stringent image registration needs of applications such as two-view attenuation correction recently developed by us. First, they achieve high accuracy ( ≈ 1.2 voxels, equivalent to 0.4 µ m) by using landmarks, rather than intensity correlations, and by using a high-dimensional affine and quadratic transformation model that accounts for 3D translation, rotation, non-isotropic scaling, modest curvature of field, distortions and mechanical inconsistencies introduced by the imaging system. Second, they use a hierarchy of models and iterative algorithms to eliminate potential instabilities. Third, they incorporate robust statistical methods to achieve accurate registration in the face of inaccurate and missing landmarks. Fourth, they are fully automated, even estimating the initial registration from the extracted landmarks. Finally, they are computationally efficient, taking less than a minute on a 900-MHz Pentium III computer for registering two images roughly 70 MB in size. The registration errors represent a combination of modelling, estimation, discretization and neuron tracing errors. Accurate 3D montaging is described; the algorithms have broader applicability to images of vasculature, and other structures with distinctive point, line and surface landmarks.

Neuron tracing in perspective

“…Although prevention is better than cure, to some extent artifacts can be removed by image processing operations such as (nonlinear) smoothing (21,22), deconvolution (20,23,24), shading correction (21,22), and morphological filtering (25)(26)(27). In applications requiring both high magnification and a large field of view, it is also often necessary to make montages (mosaics of images of partially overlapping fields), which calls for accurate image registration and stitching to avoid discontinuities (28)(29)(30).…”

Automated Analysis of Spines from Confocal Laser Microscopy Images: Application to the Discrimination of Androgen and Estrogen Effects on Spinogenesis

“…Recently a number of techniques with varying degrees of automation have been introduced (Dima et al 2002; Streekstra and van Pelt 2002; Al-Kofahi et al 2003; He et al 2003). However, most of those studies are devoted to extracting the dendrites from neuronal images.…”

Design and Control of Phase-Detection Mode Atomic Force Microscopy for Reconstruction of Cell Contours in Three Dimensions

“…In addition, topographical features can be judged only by the shades of color in OM images, due to the lack of data related to the actual height of the cell structures. Confocal microscopy (CM) is another popular microscopy method, which can provide an acceptable three-dimension image in the measured sample [3]. But, for employing the CM to inspect the biological cells, it suffers a risk from high intensive irradiation which is harmful nature to living cells [4].…”