9,314 research outputs found
Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging
© 2014 Elsevier Ltd. All rights reserved. Multi-modal image registration is becoming an increasingly powerful tool for medical diagnosis and treatment. The combination of different image modalities facilitates much greater understanding of the underlying condition, resulting in improved patient care. Mutual Information is a popular image similarity measure for performing multi-modal image registration. However, it is recognised that there are limitations with the technique that can compromise the accuracy of the registration, such as the lack of spatial information that is accounted for by the similarity measure. In this paper, we present a two-stage non-rigid registration process using a novel similarity measure, Feature Neighbourhood Mutual Information. The similarity measure efficiently incorporates both spatial and structural image properties that are not traditionally considered by MI. By incorporating such features, we find that this method is capable of achieving much greater registration accuracy when compared to existing methods, whilst also achieving efficient computational runtime. To demonstrate our method, we use a challenging medical image data set consisting of paired retinal fundus photographs and confocal scanning laser ophthalmoscope images. Accurate registration of these image pairs facilitates improved clinical diagnosis, and can be used for the early detection and prevention of glaucoma disease
Deformable Registration through Learning of Context-Specific Metric Aggregation
We propose a novel weakly supervised discriminative algorithm for learning
context specific registration metrics as a linear combination of conventional
similarity measures. Conventional metrics have been extensively used over the
past two decades and therefore both their strengths and limitations are known.
The challenge is to find the optimal relative weighting (or parameters) of
different metrics forming the similarity measure of the registration algorithm.
Hand-tuning these parameters would result in sub optimal solutions and quickly
become infeasible as the number of metrics increases. Furthermore, such
hand-crafted combination can only happen at global scale (entire volume) and
therefore will not be able to account for the different tissue properties. We
propose a learning algorithm for estimating these parameters locally,
conditioned to the data semantic classes. The objective function of our
formulation is a special case of non-convex function, difference of convex
function, which we optimize using the concave convex procedure. As a proof of
concept, we show the impact of our approach on three challenging datasets for
different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine
Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201
Towards ultra-high resolution 3D reconstruction of a whole rat brain from 3D-PLI data
3D reconstruction of the fiber connectivity of the rat brain at microscopic
scale enables gaining detailed insight about the complex structural
organization of the brain. We introduce a new method for registration and 3D
reconstruction of high- and ultra-high resolution (64 m and 1.3 m
pixel size) histological images of a Wistar rat brain acquired by 3D polarized
light imaging (3D-PLI). Our method exploits multi-scale and multi-modal 3D-PLI
data up to cellular resolution. We propose a new feature transform-based
similarity measure and a weighted regularization scheme for accurate and robust
non-rigid registration. To transform the 1.3 m ultra-high resolution data
to the reference blockface images a feature-based registration method followed
by a non-rigid registration is proposed. Our approach has been successfully
applied to 278 histological sections of a rat brain and the performance has
been quantitatively evaluated using manually placed landmarks by an expert.Comment: 9 pages, Accepted at 2nd International Workshop on Connectomics in
NeuroImaging (CNI), MICCAI'201
Groupwise Multimodal Image Registration using Joint Total Variation
In medical imaging it is common practice to acquire a wide range of
modalities (MRI, CT, PET, etc.), to highlight different structures or
pathologies. As patient movement between scans or scanning session is
unavoidable, registration is often an essential step before any subsequent
image analysis. In this paper, we introduce a cost function based on joint
total variation for such multimodal image registration. This cost function has
the advantage of enabling principled, groupwise alignment of multiple images,
whilst being insensitive to strong intensity non-uniformities. We evaluate our
algorithm on rigidly aligning both simulated and real 3D brain scans. This
validation shows robustness to strong intensity non-uniformities and low
registration errors for CT/PET to MRI alignment. Our implementation is publicly
available at https://github.com/brudfors/coregistration-njtv
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