Stitching of volumes obtained from three dimensional (3D) ultrasound (US) scanners improves visualization of anatomy
in many clinical applications. Fast but accurate volume registration remains the key challenge in this area.We propose a
volume stitching method based on efficient registration of 3D US volumes obtained from a tracked US probe. Since the
volumes, after adjusting for probe motion, are coarsely registered, we obtain salient correspondence points in the central
slices of these volumes. This is done by first removing artifacts in the US slices using intensity invariant local phase
image processing and then applying the Harris Corner detection algorithm. Fast sub-volume registration on a small
neighborhood around the points then gives fast, accurate 3D registration parameters. The method has been tested on 3D
US scans of phantom and real human radius and pelvis bones and a phantom human fetus. The method has also been
compared to volumetric registration, as well as feature based registration using 3D-SIFT. Quantitative results show
average post-registration error of 0.33mm which is comparable to volumetric registration accuracy (0.31mm) and much
better than 3D-SIFT based registration which failed to register the volumes. The proposed method was also much faster
than volumetric registration (~4.5 seconds versus 83 seconds).
Conventional voxel-based group analysis of functional magnetic resonance imaging (fMRI) data typically requires
warping each subject's brain images onto a common template to create an assumed voxel correspondence. The implicit
assumption is that aligning the anatomical structures would correspondingly align the functional regions of the subjects.
However, due to anatomical and functional inter-subject variability, mis-registration often occurs. Moreover, wholebrain
warping is likely to distort the spatial patterns of activation, which have been shown to be important markers of
task-related activation. To reduce the amount of mis-registration and distortions, warping at the brain region level has
recently been proposed. In this paper, we investigate the effects of both whole-brain and region-level warping on the
spatial patterns of activation statistics within certain regions of interests (ROIs). We have chosen to examine the bilateral
thalami and cerebellar hemispheres during a bulb-squeezing experiment, as these regions are expected to incur taskrelated
activation changes. Furthermore, the appreciable size difference between the thalamus and cerebellum allows for
exploring the effects of warping on various ROI sizes. By applying our recently proposed 3D moment-based invariant
spatial features to characterize the spatial pattern of fMRI activation statistics, we demonstrate that whole-brain warping
generally reduced discriminability of task-related activation differences. Applying the same spatial analysis to ROIs
warped at the region level showed some improvements over whole-brain warping, but warp-free analysis resulted in the
best performance. We hence suggest that spatial analysis of fMRI data that includes spatial warping to a common space
must be interpreted with caution.
KEYWORDS: Image segmentation, 3D image processing, Medical imaging, Image processing algorithms and systems, 3D image enhancement, Image quality, Binary data, Signal to noise ratio, Magnetic resonance imaging, Visualization
Segmentation of 3D data is one of the most challenging tasks in medical image analysis. While reliable automatic
methods are typically preferred, their success is often hindered by poor image quality and significant
variations in anatomy. Recent years have thus seen an increasing interest in the development of semi-automated
segmentation methods that combine computational tools with intuitive, minimal user interaction. In an earlier
work, we introduced a highly-automated technique for medical image segmentation, where a 3D extension of the
traditional 2D Livewire was proposed. In this paper, we present an enhanced and more powerful 3D Livewire-based
segmentation approach with new features designed to primarily enable the handling of complex object
topologies that are common in biological structures. The point ordering algorithm we proposed earlier, which
automatically pairs up seedpoints in 3D, is improved in this work such that multiple sets of points are allowed
to simultaneously exist. Point sets can now be automatically merged and split to accommodate for the presence
of concavities, protrusions, and non-spherical topologies. The robustness of the method is further improved by
extending the 'turtle algorithm', presented earlier, by using a turtle-path pruning step. Tests on both synthetic
and real medical images demonstrate the efficiency, reproducibility, accuracy, and robustness of the proposed
approach. Among the examples illustrated is the segmentation of the left and right ventricles from a T1-weighted
MRI scan, where an average task time reduction of 84.7% was achieved when compared to a user performing 2D
Livewire segmentation on every slice.
Registration of two dimensional to three dimensional orthopaedic medical image data has important applications
particularly in the area of image guided surgery and sports medicine. Fluoroscopy to computer tomography (CT)
registration is an important case, wherein digitally reconstructed radiographs derived from the CT data are registered to
the fluoroscopy data. Traditional registration metrics such as intensity-based mutual information (MI) typically work
well but often suffer from gross misregistration errors when the image to be registered contains a partial view of the
anatomy visible in the target image. Phase-based MI provides a robust alternative similarity measure which, in addition
to possessing the general robustness and noise immunity that MI provides, also employs local phase information in the
registration process which makes it less susceptible to the aforementioned errors. In this paper, we propose using the
complex wavelet transform for computing image phase information and incorporating that into a phase-based MI
measure for image registration. Tests on a CT volume and 6 fluoroscopy images of the knee are presented. The femur
and the tibia in the CT volume were individually registered to the fluoroscopy images using intensity-based MI,
gradient-based MI and phase-based MI. Errors in the coordinates of fiducials present in the bone structures were used to
assess the accuracy of the different registration schemes. Quantitative results demonstrate that the performance of
intensity-based MI was the worst. Gradient-based MI performed slightly better, while phase-based MI results were the
best consistently producing the lowest errors.
Extracting the brain cortex from magnetic resonance imaging (MRI) head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool (BET) produces a brain mask which may be too smooth for practical use. This paper presents a novel brain extraction tool based on three-dimensional geodesic active contours, connected component analysis and mathematical morphology. Based on user-specified intensity and contrast levels, the proposed algorithm allows an active contour to evolve naturally and extract the brain cortex. Experiments on synthetic MRI data and scanned coronal and axial MRI image volumes indicate successful extraction of tight perimeters surrounding the brain cortex. Quantitative evaluations on both synthetic phantoms and manually labeled data resulted in better accuracy than BET in terms of true and false voxel assignment. Based on these results, we illustrate that our brain extraction tool is a robust and accurate approach for the challenging task of automatically extracting the brain cortex in MRI data.
We present a fully-automated and robust microarray image analysis system for handling multi-resolution images (down
to 3-micron with sizes up to 80 MBs per channel). The system is developed to provide rapid and accurate data extraction
for our recently developed microarray analysis and quality control tool (SNP Chart). Currently available commercial
microarray image analysis applications are inefficient, due to the considerable user interaction typically required. Four-channel
DNA microarray technology is a robust and accurate tool for determining genotypes of multiple genetic markers
in individuals. It plays an important role in the state of the art trend where traditional medical treatments are to be
replaced by personalized genetic medicine, i.e. individualized therapy based on the patient's genetic heritage. However,
fast, robust, and precise image processing tools are required for the prospective practical use of microarray-based genetic
testing for predicting disease susceptibilities and drug effects in clinical practice, which require a turn-around timeline
compatible with clinical decision-making. In this paper we have developed a fully-automated image analysis platform for
the rapid investigation of hundreds of genetic variations across multiple genes. Validation tests indicate very high
accuracy levels for genotyping results. Our method achieves a significant reduction in analysis time, from several hours
to just a few minutes, and is completely automated requiring no manual interaction or guidance.
Independent Component Analysis (ICA) has proved a powerful exploratory analysis method for fMRI. In the ICA model, the fMRI data at a given time point are modeled as the linear superposition of spatially independent (and spatially stationary) component maps. The ICA model has been recently applied to positron emission tomography (PET) data with some success (Human Brain Mapping 18:284-295(2003), IEEE Trans. BME, Naganawa et al, in press). However, in PET imaging each frame is, in fact, activity integrated over a relatively long period of time, making the assumption that the underlying component maps are spatially stationary (and hence no head movement has taken place during the frame collection) very tenuous. Here we extend the application of the ICA model to 11C-methylphenidate PET data by assuming that each frame is actually composed of the superposition of rigidly transformed underlying spatial components. We first determine the “noisy” initial spatially independent components of a data set under the erroneous assumption of no intra or inter-frame motion. Aspects of the initial components that reliably track spatial perturbations of the data are then determined to produce the motion-compensated components. Initial components included ring-like spatial distributions, indicating that movement corrupts the statistical properties of the data. The final intra-frame motion-compensated components included more plausible symmetric and robust activity in the striatum as would be expected compared to the raw data and the initial components. We conclude that 1) intra-frame motion is a serious confound in PET imaging which affects the statistical properties of the data and 2) our proposed procedure ameliorates such motion effects.
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