1,562 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 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
A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images
Multicolor or multiplex fluorescence in situ
hybridization (M-FISH) imaging is a recently developed molecular
cytogenetic diagnosis technique for rapid visualization of genomic
aberrations at the chromosomal level. By the simultaneous use of
all 24 human chromosome painting probes, M-FISH imaging
facilitates precise identification of complex chromosomal
rearrangements that are responsible for cancers and genetic
diseases. The current approaches, however, cannot have the
precision sufficient for clinical use. The reliability of the
technique depends primarily on the accurate pixel-wise
classification, that is, assigning each pixel into one of the 24
classes of chromosomes based on its six-channel spectral
representations. In the paper we introduce a novel approach to
improve the accuracy of pixel-wise classification. The approach is
based on the combination of fuzzy clustering and wavelet
normalization. Two wavelet-based algorithms are used to reduce
redundancies and to correct misalignments between multichannel
FISH images. In comparison with conventional algorithms, the
wavelet-based approaches offer more advantages such as the
adaptive feature selection and accurate image registration. The
algorithms have been tested on images from normal cells, showing
the improvement in classification accuracy. The increased accuracy
of pixel-wise classification will improve the reliability of the
M-FISH imaging technique in identifying subtle and cryptic
chromosomal abnormalities for cancer diagnosis and genetic
disorder research
Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections
Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE
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
- …