5,274 research outputs found
Locally Orderless Registration
Image registration is an important tool for medical image analysis and is
used to bring images into the same reference frame by warping the coordinate
field of one image, such that some similarity measure is minimized. We study
similarity in image registration in the context of Locally Orderless Images
(LOI), which is the natural way to study density estimates and reveals the 3
fundamental scales: the measurement scale, the intensity scale, and the
integration scale.
This paper has three main contributions: Firstly, we rephrase a large set of
popular similarity measures into a common framework, which we refer to as
Locally Orderless Registration, and which makes full use of the features of
local histograms. Secondly, we extend the theoretical understanding of the
local histograms. Thirdly, we use our framework to compare two state-of-the-art
intensity density estimators for image registration: The Parzen Window (PW) and
the Generalized Partial Volume (GPV), and we demonstrate their differences on a
popular similarity measure, Normalized Mutual Information (NMI).
We conclude, that complicated similarity measures such as NMI may be
evaluated almost as fast as simple measures such as Sum of Squared Distances
(SSD) regardless of the choice of PW and GPV. Also, GPV is an asymmetric
measure, and PW is our preferred choice.Comment: submitte
Robust and Fast 3D Scan Alignment using Mutual Information
This paper presents a mutual information (MI) based algorithm for the
estimation of full 6-degree-of-freedom (DOF) rigid body transformation between
two overlapping point clouds. We first divide the scene into a 3D voxel grid
and define simple to compute features for each voxel in the scan. The two scans
that need to be aligned are considered as a collection of these features and
the MI between these voxelized features is maximized to obtain the correct
alignment of scans. We have implemented our method with various simple point
cloud features (such as number of points in voxel, variance of z-height in
voxel) and compared the performance of the proposed method with existing
point-to-point and point-to- distribution registration methods. We show that
our approach has an efficient and fast parallel implementation on GPU, and
evaluate the robustness and speed of the proposed algorithm on two real-world
datasets which have variety of dynamic scenes from different environments
2D Reconstruction of Small Intestine's Interior Wall
Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless endoscopic image stitching, let alone systematic investigation. The
proposed new wireless endoscopic image stitching method consists of two main
steps to improve the accuracy and efficiency of image registration. First, the
keypoints are extracted by Principle Component Analysis and Scale Invariant
Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood
Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable
keypoints. Second, the optimal transformation parameters obtained from first
step are fed to the Normalised Mutual Information (NMI) algorithm as an initial
solution. With modified Marquardt-Levenberg search strategy in a multiscale
framework, the NMI can find the optimal transformation parameters in the
shortest time. The proposed methodology has been tested on two different
datasets - one with real wireless endoscopic images and another with images
obtained from Micro-Ball (a new wireless cubic endoscopy system with six image
sensors). The results have demonstrated the accuracy and robustness of the
proposed methodology both visually and quantitatively.Comment: Journal draf
Robust Algorithms for Registration of 3D Images of Human Brain
This thesis is concerned with the process of automatically aligning 3D medical images of human brain. It concentrates on rigid-body matching of Positron Emission Tomography images (PET) and Magnetic Resonance images (MR) within one patient and on non-linear matching of PET images of different patients. In recent years, mutual information has proved to be an excellent criterion for automatic registration of intra-individual images from different modalities. We propose and evaluate a method that combines a multi-resolution optimization of mutual information with an efficient segmentation of background voxels and a modified principal axes algorithm. We show that an acceleration factor of 6-7 can be achieved without loss of accuracy and that the method significantly reduces the rate of unsuccessful registrations. Emphasis was also laid on creation of an automatic registration system that could be used routinely in clinical environment. Non-linear registration tries to reduce the inter-individual variability of shape and structure between two brain images by deforming one image so that homologous regions in both images get aligned. It is an important step of many procedures in medical image processing and analysis. We present a novel algorithm for an automatic non-linear registration of PET images based on hierarchical volume subdivisions and local affine optimizations. It produces a C2-continuous deformation function and guarantees that the deformation is one-to-one. Performance of the algorithm was evaluated on more than 600 clinical PET images
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