6,578 research outputs found
Registration of Standardized Histological Images in Feature Space
In this paper, we propose three novel and important methods for the
registration of histological images for 3D reconstruction. First, possible
intensity variations and nonstandardness in images are corrected by an
intensity standardization process which maps the image scale into a standard
scale where the similar intensities correspond to similar tissues meaning.
Second, 2D histological images are mapped into a feature space where continuous
variables are used as high confidence image features for accurate registration.
Third, we propose an automatic best reference slice selection algorithm that
improves reconstruction quality based on both image entropy and mean square
error of the registration process. We demonstrate that the choice of reference
slice has a significant impact on registration error, standardization, feature
space and entropy information. After 2D histological slices are registered
through an affine transformation with respect to an automatically chosen
reference, the 3D volume is reconstructed by co-registering 2D slices
elastically.Comment: SPIE Medical Imaging 2008 - submissio
Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity
Non-rigid registration is challenging because it is ill-posed with high
degrees of freedom and is thus sensitive to noise and outliers. We propose a
robust non-rigid registration method using reweighted sparsities on position
and transformation to estimate the deformations between 3-D shapes. We
formulate the energy function with position and transformation sparsity on both
the data term and the smoothness term, and define the smoothness constraint
using local rigidity. The double sparsity based non-rigid registration model is
enhanced with a reweighting scheme, and solved by transferring the model into
four alternately-optimized subproblems which have exact solutions and
guaranteed convergence. Experimental results on both public datasets and real
scanned datasets show that our method outperforms the state-of-the-art methods
and is more robust to noise and outliers than conventional non-rigid
registration methods.Comment: IEEE Transactions on Visualization and Computer Graphic
Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG--MRI
With a hybrid MEG--MRI device that uses the same sensors for both modalities,
the co-registration of MRI and MEG data can be replaced by an automatic
calibration step. Based on the highly accurate signal model of ultra-low-field
(ULF) MRI, we introduce a calibration method that eliminates the error sources
of traditional co-registration. The signal model includes complex sensitivity
profiles of the superconducting pickup coils. In ULF MRI, the profiles are
independent of the sample and therefore well-defined. In the most basic form,
the spatial information of the profiles, captured in parallel ULF-MR
acquisitions, is used to find the exact coordinate transformation required. We
assessed our calibration method by simulations assuming a helmet-shaped
pickup-coil-array geometry. Using a carefully constructed objective function
and sufficient approximations, even with low-SNR images, sub-voxel and
sub-millimeter calibration accuracy was achieved. After the calibration,
distortion-free MRI and high spatial accuracy for MEG source localization can
be achieved. For an accurate sensor-array geometry, the co-registration and
associated errors are eliminated, and the positional error can be reduced to a
negligible level.Comment: 11 pages, 8 figures. This work is part of the BREAKBEN project and
has received funding from the European Union's Horizon 2020 research and
innovation programme under grant agreement No 68686
Interpretable Transformations with Encoder-Decoder Networks
Deep feature spaces have the capacity to encode complex transformations of
their input data. However, understanding the relative feature-space
relationship between two transformed encoded images is difficult. For instance,
what is the relative feature space relationship between two rotated images?
What is decoded when we interpolate in feature space? Ideally, we want to
disentangle confounding factors, such as pose, appearance, and illumination,
from object identity. Disentangling these is difficult because they interact in
very nonlinear ways. We propose a simple method to construct a deep feature
space, with explicitly disentangled representations of several known
transformations. A person or algorithm can then manipulate the disentangled
representation, for example, to re-render an image with explicit control over
parameterized degrees of freedom. The feature space is constructed using a
transforming encoder-decoder network with a custom feature transform layer,
acting on the hidden representations. We demonstrate the advantages of explicit
disentangling on a variety of datasets and transformations, and as an aid for
traditional tasks, such as classification.Comment: Accepted at ICCV 201
A factorization approach to inertial affine structure from motion
We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives
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