Alzheimer’s disease (AD) is the most common cause of dementia. It is characterized by irreversible memory loss and degradation of cognitive skills. Amyloid PET imaging has been used in the diagnosis of AD to measure the amyloid burden in the brain. It is quantified by the Standard Uptake Value Ratio (SUVR). However, there is great variability in SUVR measurements when different scanner models are used. Therefore, standardization and harmonization is required for quantitative assessments of amyloid PET scans in a multi-center or longitudinal study. Conventionally, PET image harmonization has been tackled either by standardization protocols at the time of image reconstruction, or by applying a smoothing function to bring PET images to a common resolution using phantom data. In this work, we propose an automatic approach that aims to match the data distribution of PET images through unsupervised learning. To that end, we propose Smoothing-CycleGAN, a modified cycleGAN that uses a 3D smoothing kernel to learn the optimum Point Spread Function (PSF) for bringing PET images into a common spatial resolution. We validate our approach using two sets of datasets, and we analyze the SUVR agreement before and after PET image harmonization. Our results show that the PSF of PET images that have different spatial resolutions can be estimated automatically using Smoothing-cycleGAN, which results in better SUVR agreement after image translation.
With the advances of PET tracers for β-Amyloid (Aβ) detection in neurodegenerative diseases, automated quantification methods are desirable. For clinical use, there is a great need for PET-only quantification method, as MR images are not always available. In this paper, we validate a previously developed PET-only quantification method against MR-based quantification using 6 tracers: 18F-Florbetaben (N=148), 18F-Florbetapir (N=171), 18F-NAV4694 (N=47), 18F-Flutemetamol (N=180), 11C-PiB (N=381) and 18F-FDG (N=34). The results show an overall mean absolute percentage error of less than 5% for each tracer. The method has been implemented as a remote service called CapAIBL (http://milxcloud.csiro.au/capaibl). PET images are uploaded to a cloud platform where they are spatially normalised to a standard template and quantified. A report containing global as well as local quantification, along with surface projection of the β-Amyloid deposition is automatically generated at the end of the pipeline and emailed to the user.
In order to fit an unseen surface using statistical shape model (SSM), a correspondence between the unseen
surface and the model needs to be established, before the shape parameters can be estimated based on this
correspondence. The correspondence and parameter estimation problem can be modeled probabilistically by
a Gaussian mixture model (GMM), and solved by expectation-maximization iterative closest points (EM-ICP)
algorithm. In this paper, we propose to exploit the linearity of the principal component analysis (PCA) based
SSM, and estimate the parameters for the unseen shape surface under the EM-ICP framework. The symmetric
data terms are devised to enforce the mutual consistency between the model reconstruction and the shape surface.
The a priori shape information encoded in the SSM is also included as regularization. The estimation method
is applied to the shape modeling of the hippocampus using a hippocampal SSM.
KEYWORDS: Alzheimer's disease, Expectation maximization algorithms, Medical research, Image segmentation, Magnetic resonance imaging, Brain, 3D image processing, Visualization, Positron emission tomography, In vivo imaging
β-amyloid has been shown to play a crucial role in Alzheimer's disease (AD). In vivo β-amyloid imaging using
[11C]Pittsburgh compound Β (PiB) positron emission tomography has made it possible to analyze the relationship
between β-amyloid deposition and different pathological markers involved in AD. PiB allows us to stratify the
population between subjects which are likely to have prodromal AD, and those who don't. The comparison of the
cortical thickness in these different groups is important to better understanding and detect the first symptoms of the
disease which may lead to an earlier therapeutic care to reduce neurone loss.
Several techniques have been developed to compare the cortical volume and/or thickness between AD and HC groups.
However due to the noise introduced by the cortical thickness estimation and by the registration, these methods do not
allow to unveil any major different when comparing prodromal AD groups with healthy control subjects group. To
improve our understanding of where initial Alzheimer neurodegeneration occurs in the cortex we have developed a
surface based technique, and have applied it to the discrimination between PIB-positive and PiB-negative HCs. We first
identify the regions where AD patients show high cortical atrophy by using an AD/PiB- HC vertex-wise T-test. In each
of these discriminating regions, comparison between PiB+ HC, PiB- HC and AD are performed. We found some
significant differences between the two HC groups in the hippocampus and in the temporal lobe for both hemisphere and
in the precuneus and occipital regions only for the left hemisphere.
The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). Using brain
Magnetic Resonance (MR) images, we can investigate the effect of AD on the morphology of the hippocampus.
Statistical shape models (SSM) are usually used to describe and model the hippocampal shape variations among
the population. We use the shape variation from SSM as features to classify AD from normal control cases
(NC). Conventional SSM uses principal component analysis (PCA) to compute the modes of variations among
the population. Although these modes are representative of variations within the training data, they are not
necessarily discriminant on labelled data. In this study, a Hotelling's T 2 test is used to qualify the landmarks
which can be used for PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination
ability of these predictors is evaluated in terms of their classification performances using support vector
machines (SVM). Using only landmarks statistically discriminant between AD and NC in SSM showed a better
separation between AD and NC. These predictors also showed better correlation to the cognitive scores such as
mini-mental state examination (MMSE) and Alzheimer's disease assessment scale (ADAS).
In multi-atlas based image segmentation, multiple atlases with label maps are propagated to the query image, and fused into the segmentation result. Voting rule is commonly used classifier fusion method to produce the consensus map. Local weighted voting (LWV) is another method which combines the propagated atlases weighted by local image similarity. When LWV is used, we found that the segmentation accuracy converges slower comparing to simple voting rule. We therefore propose to introduce diversity in addition to image similarity by using Maximal Marginal Relevance (MMR) criteria as a more efficient way to rank and select atlases. We test the MMR re-ranking on a hippocampal atlas set of 138 normal control (NC) subjects and another set of 99 Alzheimer's disease patients provided by ADNI. The result shows that MMR re-ranking performed better than similarity based atlas selection when same number of atlases were selected.
Small animal image registration is challenging because of its joint structure, and posture and position difference in each
acquisition without a standard scan protocol. In this paper, we face the issue of mouse whole-body skeleton registration
from CT images. A novel method is developed for analyzing mouse hind-limb and fore-limb postures based on geodesic
path descriptor and then registering the major skeletons and fore limb skeletons initially by thin-plate spline (TPS)
transform based on the obtained geodesic paths and their enhanced correspondence fields. A target landmark correction
method is proposed for improving the registration accuracy of the improved 3D shape context non-rigid registration
method we previously proposed. A novel non-rigid registration framework, combining the skeleton posture analysis,
geodesic path based initial alignment and 3D shape context model, is proposed for mouse whole-body skeleton
registration. The performance of the proposed methods and framework was tested on 12 pairs of mouse whole-body
skeletons. The experimental results demonstrated the flexibility, stability and accuracy of the proposed framework for
automatic mouse whole body skeleton registration.
Automatic small animal whole-body organ registration is challenging because of subject's joint structure, posture and
position difference and loss of reference features. In this paper, an improved 3D shape context based non-rigid
registration method is applied for mouse whole-body skeleton registration and lung registration. A geodesic path based
non-rigid registration method is proposed for mouse torso skin registration. Based on the above registration methods, a
novel non-rigid registration framework is proposed for mouse whole-body organ mapping from an atlas to new scanned
CT data. A preliminary experiment was performed to test the method on lung and skin registration. A whole-body organ
mapping was performed on three target data and the selected organs were compared with the manual outlining results.
The robust of the method has been demonstrated.
3D shape context is a method to define matching points between similar shapes as a pre-processing step to non-rigid
registration. The main limitation of the approach is point mismatching, which includes long geodesic distance mismatch
and neighbors crossing mismatch. In this paper, we propose a topological structure verification method to correct the
long geodesic distance mismatch and a correspondence field smoothing method to correct the neighbors crossing
mismatch. A robust 3D shape context model is proposed and further combined with thin-plate spline model for non-rigid
surface registration. The method was tested on phantoms and rat hind limb skeletons from micro CT images. The results
from experiments on mouse hind limb skeletons indicate that the approach is robust.
Multiatlas based segmentation-propagation approaches have been shown to obtain accurate parcelation of brain
structures. However, this approach requires a large number of manually delineated atlases, which are often not
available. We propose a supervised method to build a population specific atlas database, using the publicly
available Internet Brain Segmentation Repository (IBSR). The set of atlases grows iteratively as new atlases
are added, so that its segmentation capability may be enhanced in the multiatlas based approach. Using a
dataset of 210 MR images of elderly subjects (170 elderly control, 40 Alzheimer's disease) from the Australian
Imaging, Biomarkers and Lifestyle (AIBL) study, 40 MR images were segmented to build a population specific
atlas database for the purpose of multiatlas segmentation-propagation. The population specific atlases were used
to segment the elderly population of 210 MR images, and were evaluated in terms of the agreement among the
propagated labels. The agreement was measured by using the entropy H of the probability image produced
when fused by voting rule and the partial moment μ2 of the histogram. Compared with using IBSR atlases, the
population specific atlases obtained a higher agreement when dealing with images of elderly subjects.
Micro-CT/PET imaging scanner provides a powerful tool to study tumor in small rodents in response to therapy.
Accurate image registration is a necessary step to quantify the characteristics of images acquired in longitudinal studies.
Small animal registration is challenging because of the very deformable body of the animal often resulting in different
postures despite physical restraints. In this paper, we propose a non-rigid registration approach for the automatic
registration of mouse whole body skeletons, which is based on our improved 3D shape context non-rigid registration
method. The whole body skeleton registration approach has been tested on 21 pairs of mouse CT images with variations
of individuals and time-instances. The experimental results demonstrated the stability and accuracy of the proposed
method for automatic mouse whole body skeleton registration.
This paper presents a novel method to reduce the effects of interleaving motion artefacts in single-plane MR scanning of
the pelvic region without the need for k-space information. Interleaved image (or multipacket) acquisition is frequently
used to reduce cross-talk and scanning time during full pelvic MR scans. Patient motion during interleaved acquisition
can result in non-linear "staircase" imaging artefacts which are most visible on sagittal and coronal reconstructions.
These artefacts can affect the segmentation of organs, registration, and visualization. A fast method has been
implemented to replace artefact affected slices in a packet with interpolated slices based on Penney et al (2004) whose
method involves the registration of neighbouring slices to obtain correspondences, followed by linear interpolation of
voxel intensities along the displacement fields. This interpolation method has been applied to correct motion affected
MRI volumes by firstly creating a new volume where every axial slice from the artefact affected packet is removed and
replaced with an interpolated slice and then secondly for each of these slices, 2D non-rigid registration is used to register
each original axial slice back to its matching interpolated slice. Results show visible improvements in artefacts
particularly in sagittal and coronal image reconstructions, and should result in improved intensity based non-rigid
registration results between MR scans (for example for atlas based automatic segmentation). Further validation was
performed on simulated interleaving artefacts which were applied to an artefact free volume. Results obtained on
prostate cancer radiotherapy treatment planning contouring were inconclusive and require further investigation.
Small animal registration is an important step for molecular image analysis. Skeleton registration from whole-body or
only partial micro Computerized Tomography (CT) image is often performed to match individual rats to atlases and
templates, for example to identify organs in positron emission tomography (PET). In this paper, we extend the shape
context matching technique for 3D surface registration and apply it for rat hind limb skeleton registration from CT
images. Using the proposed method, after standard affine iterative closest point (ICP) registration, correspondences
between the 3D points from sour and target objects were robustly found and used to deform the limb skeleton surface
with thin-plate-spline (TPS). Experiments are described using phantoms and actual rat hind limb skeletons. On animals,
mean square errors were decreased by the proposed registration compared to that of its initial alignment. Visually,
skeletons were successfully registered even in cases of very different animal poses.
Despite the increasing use of 11C-PiB in research into Alzheimer's disease (AD), there are few standardized analysis
procedures that have been reported or published. This is especially true with regards to partial volume effects (PVE) and
partial volume correction. Due to the nature of PET physics and acquisition, PET images exhibit relatively low spatial
resolution compared to other modalities, resulting in bias of quantitative results. Although previous studies have applied PVE correction techniques on 11C-PiB data, the results have not been quantitatively evaluated and compared against uncorrected data. The aim of this study is threefold. Firstly, a realistic
synthetic phantom was created to quantify PVE. Secondly, MRI partial volume estimate segmentations were used to improve voxel-based PVE correction instead of using hard segmentations. Thirdly, quantification of PVE correction was evaluated on 34 subjects (AD=10, Normal Controls (NC)=24), including 12 PiB positive NC. Regional analysis was performed using the Anatomical Automatic Labeling (AAL) template, which was registered to each patient. Regions of interest were restricted to the gray matter (GM) defined by the MR segmentation. Average normalized intensity of the neocortex and selected regions were used to evaluate the discrimination power between AD and NC both with and without PVE correction. Receiver Operating Characteristic (ROC) curves were computed for the binary discrimination task. The phantom study revealed signal losses due to PVE between 10 to 40 % which were mostly recovered to within 5% after correction. Better classification was achieved after PVE correction, resulting in higher areas under ROC curves.
Measurement of the cortical thickness from 3D Magnetic Resonance Imaging (MRI) can aid diagnosis and
longitudinal studies of a wide range of neurodegenerative diseases. We estimate the cortical thickness using a
Laplacian approach whereby equipotentials analogous to layers of tissue are computed. The thickness is then
obtained using an Eulerian approach where partial differential equations (PDE) are solved, avoiding the explicit
tracing of trajectories along the streamlines gradient. This method has the advantage of being relatively fast
and insure unique correspondence points between the inner and outer boundaries of the cortex. The original
method is challenged when the thickness of the cortex is of the same order of magnitude as the image resolution
since partial volume (PV) effect is not taken into account at the gray matter (GM) boundaries. We propose
a novel way to take into account PV which improves substantially accuracy and robustness. We model PV by
computing a mixture of pure Gaussian probability distributions and use this estimate to initialize the cortical
thickness estimation. On synthetic phantoms experiments, the errors were divided by three while reproducibility
was improved when the same patients was scanned three consecutive times.
We describe a new framework for measuring cortical thickness from MR human brain images. This involves the
integration of a method of tissue classification with one to estimate thickness in 3D. We have determined an additional
boundary detection step to facilitate this. The classification stage utlizes the Expectation Maximisation
(EM) algorithm to classify voxels associated with the tissue types that interface with cortical grey matter (GM,
WM and CSF). This uses a Gaussian mixture and the EM algorithm to estimate the position and and width
of the Gaussians that model the intensity distributions of the GM, WM and CSF tissue classes. The boundary
detection stage uses the GM, WM and CSF classifications and finds connected components, fills holes and then
applies a geodesic distance transform to determine the GM/WM interface. Finally the thickness of the cortical
grey matter is estimated by solving Laplace's equation and determining the streamlines that connect the inner
and outer boundaries. The contribution of this work is the adaptation of the classification and thickness measurement
steps, neither requiring manual initialisation, and also the validation strategy. The resultant algorithm
is fully automatic and avoids the computational expense associated with preserving the cortical surface topology.
We have devised a validation strategy that indicates the cortical segmentation of a gold standard brain atlas
has a similarity index of 0.91, thickness estimation has subvoxel accuracy evaluated using a synthetic image and
precision of the combined segmentation and thickness measurement of 1.54mm using three clinical images.
The segmentation of the bones from MR images is useful for performing subsequent segmentation and quantitative
measurements of cartilage tissue. In this paper, we present a shape based segmentation scheme for the bones
that uses texture features derived from the phase and intensity information in the complex MR image. The
phase can provide additional information about the tissue interfaces, but due to the phase unwrapping problem,
this information is usually discarded. By using a Gabor filter bank on the complex MR image, texture features
(including phase) can be extracted without requiring phase unwrapping. These texture features are then analyzed
using a support vector machine classifier to obtain probability tissue matches. The segmentation of the bone is
fully automatic and performed using a 3D active shape model based approach driven using gradient and texture
information. The 3D active shape model is automatically initialized using a robust affine registration. The
approach is validated using a database of 18 FLASH MR images that are manually segmented, with an average
segmentation overlap (Dice similarity coefficient) of 0.92 compared to 0.9 obtained using the classifier only.
This paper focuses on reviewing some recent works of the use of Gabor filters dealing with industrial applications. After a brief recall of Gabor filter basis, the two usual uses of Gabor filters are recalled: filter bank approach and filter design approach. The third part presents recent published works domain by domain. A fourth part exposes our own work with Gabor Filters for defect detection on semiconductor. A short conclusion summarizes the paper.
In the last decade, the accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using microscopy. This technique allows capturing a complex image of a scene (i.e. containing magnitude and phase), and reconstructing the phase and magnitude information. Digital holograms give a new dimension to texture analysis since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on image segmentation of patterned wafers for defect detection. This paper presents a combination of features obtained using Gabor filtering on different complex images. The combination enables to cope with the intensity variations occurring during the holography and provides final results which are independent from the selected training samples.
In the last decade, the accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using microscopy. This technique allows capturing a complex image of a scene (i.e. containing magnitude and phase), and reconstructing the phase and magnitude information. Digital holograms give a new dimension to texture analysis since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on image segmentation of patterned wafers for defect detection. This paper presents a comparison between the features obtained using Gabor filtering on complex images under illumination and focus variations.
We extend our previous work on the image segmentation of electronic structures on patterned wafers to improve the defect detection process on optical inspection tools. Die-to-die wafer inspection is based on the comparison of the same area on two neighboring dies. The dissimilarities between the images are a result of defects in this area of one of the dies. The noise level can vary from one structure to the other, within the same image. Therefore, segmentation is required to create a mask and apply an optimal threshold in each region. Contrast variation on the texture can affect the response of the parameters used for the segmentation. We show a method to anticipate these variations with a limited number of training samples, and modify the classifier accordingly to improve the segmentation results.
In the last decade, the accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using a microscopy. This technique allows capturing a complex image of a scene, and reconstructing the phase and magnitude information. This type of image gives a new dimension to texture analysis since the topology information can be used as an additional way to extract features. This new technique can be used to extend our previous work on image segmentation of patterned wafers for defect detection. This paper presents a comparison between the features obtained using Gabor filtering on complex (i.e. containing magnitude and phase) images under illumination and focus variations.
This paper introduces a segmentation algorithm suitable for semiconductor wafer images generated by optical inspection tools. The primary application of this work is content-based region segmentation for automatic threshold selection during recipe generation in die-to-die wafer inspection. Structures associated with different functional areas lead to different levels of noise in the difference image during the defect detection process. The ability to automatically create a mask to separate the different structures and materials is necessary to determine local thresholds for each area and thus to improve the signal-to-noise ratio. A supervised segmentation based on the discrete wavelet transform is used to segment a whole die to create a mask. During the inspection, the mask is applied on the difference image, and the threshold is automatically set as a function of the noise within the region and the thresholding coefficient specific to that region. Preliminary segmentation results are very promising. The use of the segmented region in content-based threshold defect detection improves the number of defects detected, and reduces the number of false detections. This paper will show the performance of the segmentation method on optical microscope wafer images, and the subsequent improvement of the defect detection process.
In this paper, we propose a method of implementation improvement of the decision rule of the support vector machine, applied to real-time image segmentation. We present very high speed decisions (approximately 10 ns per pixel) which can be useful for detection of anomalies on manufactured parts. We propose an original combination of classifiers allowing fast and robust classification applied to image segmentation. The SVM is used during a first step, pre-processing the training set and thus rejecting any ambiguities. The hyperrectangles-based learning algorithm is applied using the SVM classified training set. We show that the hyperrectangle method imitates the SVM method in terms of performances, for a lower cost of implementation using reconfigurable computing. We review the principles of the two classifiers: the Hyperrectangles-based method and the SVM and we present our combination method applied on image segmentation of an industrial part.
This paper is an extension of our previous work on the image segmentation of electronic structures on patterned wafers to improve the defect detection process on optical inspection tools. Die-to-die wafer inspection is based upon the comparison of the same area on two neighborhood dies. The dissimilarities between the images are a result of defects in this area of one of the die. The noise level can vary from one structure to the other, within the same image. Therefore, segmentation is needed to create a mask and apply an optimal threshold in each region. Contrast variation on the texture can affect the response of the parameters used for the segmentation. This paper shows a method to anticipate these variations with a limited number on training samples, and modify the classifier accordingly to improve the segmentation results.
The topic of this research is to the study the feasibility of a machine vision prototype for the control of metallic tubes (used in water pomp). Nine different kinds of defects located everywhere on the tube have to be detected: The defects are: on the top: little hollows, bumps, and excesses of material on the body: horizontal bumps, vertical bumps, vertical scratches and finally on the bottom: vertical ridges, holes, and bumps. As the defects on the top of the tube are very small, a grazing angle is used to light the tube. The camera is set on the opposite side of the tube with the same angle. Hollows and bumps are both detected by a vertical Sobel gradient. For the third defects, the excess of material projects its shadow on the top of the tube, and defects are detected by looking for a dark region instead of a lighted one. To inspect the rest of the tubes, a neon tube with a diffuser is employed to homogeneously light the body and the bottom of the tubes. Association of gradient operators, threshold procedures enables to find all the defects.
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