In tensor-based morphometry (TBM) group-wise differences in brain structure are measured using high degreeof-
freedom registration and some form of statistical test. However, it is known that TBM results are sensitive to
both the registration method and statistical test used. Given the lack of an objective model of group variation is
it difficult to determine a best registration method for TBM. The use of statistical tests is also problematic given
the corrections required for multiple testing and the notorius difficulty selecting and intepreting signigance values.
This paper presents an approach to address both of these issues by combining multiple registration methods using
Dempster-Shafer Evidence theory to produce belief maps of categorical changes between groups. This approach
is applied to the comparison brain morphometry in aging, a typical application of TBM, using the determinant
of the Jacobian as a measure of volume change. We show that the Dempster-Shafer combination produces a
unique and easy to interpret belief map of regional changes between and within groups without the complications
associated with hypothesis testing.
A major focus of computational anatomy is to extract the most relevant information to identify and characterize
anatomical variability within a group of subjects as well as between different groups. The construction of atlases
is central to this effort. An atlas is a deterministic or probabilistic model with intensity variance, structural,
functional or biochemical information over a population. To date most algorithms to construct atlases have
been based on a single subject assuming that the population is best described by a single atlas. However, we
believe that in a population with a wide range of subjects multiple atlases may be more representative since
they reveal the anatomical differences and similarities within the group. In this work, we propose to use the
K-means clustering algorithm to partition a set of images into several subclasses, based on a joint distance which
is composed of a distance quantifying the deformation between images and a dissimilarity measured from the registration residual. During clustering, the spatial transformations are averaged rather than images to form cluster centers, to ensure a crisp reference. At the end of this algorithm, the updated centers of the k clusters
are our atlases. We demonstrate this algorithm on a subset of a public available database with whole brain
volumes of subjects aged 18-96 years. The atlases constructed by this method capture the significant structural
differences across the group.
The recent driven equilibrium single-pulse observation of T1 (DESPOT1) approach permits real-time clinical
acquisition of large-volume and high-isotropic-resolution T1 mapping of MR tissue parameters with improved
uniformity. It is assumed that the quantitative nature of maps will facilitate clinical applications such as disease
diagnosis and comparison across subjects. However, there is not yet enough quantitative evidence on the
actual benefit of adopting T1 maps, especially in computer-aided medical image analysis tasks. In this study, we
compare methods with respect to image types, T1-weighted images or T1 maps, in automatic brain MRI segmentation.
Our experimental results demonstrate that, using T1 maps, different segmentation algorithms show
better agreement with each other, compared to that from using T1-weighted images. Furthermore, through
multi-dimensional-scaling projection, we are able to visualize the relative affinity among segmentation results,
which reveals that the projections of those segmentations using two different types of input images tend to form
two separate clusters. Finally, by comparing to expert segmented reference segmentation of brain sub-regions,
our results clearly indicate a better agreement between the manual reference and those automatic ones on T1
maps. In other words, our study provides an evidence for the hypothesis that compared to the conventionally
used T1-weighted images, T1 maps lead to improved reliability in automatic brain MRI segmentation task.
X-ray imaging is of paramount importance for clinical and pre-clinical applications but it is fundamentally restricted by the attenuation-based contrast mechanism, which has remained essentially the same since Roentgen's discovery a century ago. Recently, based on the Talbot effect, groundbreaking work was reported using 1D gratings for x-ray phase-contrast imaging with a hospital-grade x-ray tube instead of a synchrotron or micro-focused source. In this paper, we
report an extension of our earlier 2D-grating-based work to the case of Gaussian beams. This 2D-grating-based approach has the potential to reduce the imaging time, increase the spatial coherence, and enhance the accuracy and robustness compared to current 1D-grating-based phase-contrast imaging techniques.
Differential Phase Contrast Imaging (DPCI) has the potential to vastly increase soft tissue contrast. DPCI requires spatial and temporal coherence as generated by a synchrotron or a micro-focus
X-ray source; however, recent research demonstrates DPCI can be implemented using a conventional X-ray source with three transmission gratings (Pfeiffer et al., Nature 2006). This paper describes the optimization of the essential system parameters (system size, delivered dose, spatial resolution) of this implementation from a theoretical perspective. The optimization of these parameters is an essential step in practical application of DPCI. We conclude that the minimum size of the system is approximately 700 mm, the minimum resolution is 100 um, and the dose is 1/1000 that of conventional absorption CT.
CT colonography is a minimally-invasive screening technique for colorectal polyps in which X-ray CT images of the
distended colon are acquired, usually in the prone and supine positions. Registration of segmented colons from both
images will be useful for computer-assisted polyp detection. We have previously presented algorithms for registration of
the prone and supine colon when both are well distended and there is a single connected lumen. However due to
inadequate bowel preparation or peristalsis there may be collapsed segments in one or both of the colons resulting in a
topological change in the images. Such changes make deformable registrations of the colons difficult, and at present
there are no registration algorithms which can accommodate them. In this paper we present an algorithm which can
perform volume registration of prone/supine colon images in the presence of a topological change.
The Geometric Deformable Model is developed for accurate colon lumen segmentation as part of an automatic Virtual Colonoscopy system. The deformable model refines the lumen surface found by an automatic seed location and thresholding procedure. The challenges to applying the deformable model are described, showing the definition of the stopping function as the key to accurate segmentation. The limitations of current stopping criteria are examined and a new definition, tailored to the task of colon segmentation, is given. First, a multiscale edge operator is used to locate high confidence boundaries. These boundaries are then integrated into the stopping function using a distance transform. The hypothesis is that the new stopping function results in a more accurate representation of the lumen surface compared to previous monotonic functions of the gradient magnitude. This hypothesis is tested using observer ratings of colon surface fidelity at 100 hundred randomly selected locations in each of four datasets. The results show that the surfaces determined by the modified deformable model better represent the lumen surface overall.
Proc. SPIE. 4322, Medical Imaging 2001: Image Processing
KEYWORDS: Signal to noise ratio, Edge detection, Sensors, Magnetic resonance imaging, Image segmentation, Error analysis, Image analysis, Medical imaging, Signal processing, Filtering (signal processing)
The scale of interesting structures in medical images is space variant because of partial volume effects, spatial dependence of resolution in many imaging modalities, and differences in tissue properties. Existing segmentation methods either apply a single scale to the entire image or try fine-to-coarse/coarse-to-fine tracking of structures over multiple scales. While single scale approaches fail to fully recover the perceptually important structures, multi-scale methods have problems in providing reliable means to select proper scales and integrating information over multiple scales. A recent approach proposed by Elder and Zucker addresses the scale selection problem by computing a minimal reliable scale for each image pixel. The basic premise of this approach is that, while the scale of structures within an image vary spatially, the imaging system is fixed. Hence, sensor noise statistics can be calculated. Based on a model of edges to be detected, and operators to be used for detection, one can locally compute a unique minimal reliable scale at which the likelihood of error due to sensor noise is less than or equal to a predetermined threshold. In this paper, we improve the segmentation method based on the minimal reliable scale selection and evaluate its effectiveness with both simulated and actual medical data.
The Geometric Deformable Model (GDM) is a useful segmentation method that combines the energy minimization concepts of physically deformable models and the flexible topology of implicit deformable models in a mathematically well-defined framework. The key aspect of the method is the measurement of length and area using a conformal metric derived from the image. This conformal metric, usually a monotonicly decreasing function of the gradient, defines a Riemannian space in which the surface evolves. The success of the GDM for 3D segmentation in medical applications is directly related to the definition of the conformal metric. Like all deformable models, the GDM is susceptible to poor initialization, varying contrast, partial volume, and noise. This paper addresses these difficulties via the definition of the conformal metric and describes a new method for computing the metric in 3D. This method, referred to as a confidence-based mapping, incorporates a new 3D scale selection mechanism and an a-priori image model. A comparison of the confidence-based approach and previous formulations of the conformal metric is presented using computer phantoms. A preliminary application in two clinical examples is given.
Proc. SPIE. 4121, Mathematical Modeling, Estimation, and Imaging
KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Human-machine interfaces, Magnetic resonance imaging, Image segmentation, Interfaces, Control systems, Computer simulations, Image analysis, Medical imaging
The geometric deformable model (GDM) determines object boundaries by evolving initial interfaces along the normal direction. A speed function controls how fast the interfaces move. When the speed function is zero or sufficiently small, the evolution stops or slows down significantly. Because the gradient flow equation that governs a GDM's evolution can be easily implemented with the level set technique, the GDM has the distinct advantage of being topologically flexible. Since its inception, the GDM has been successfully applied to many applications in medical imaging where variable geometry and topology of the model is crucial. Although much work has been done to improve and extend this method, little attention has been paid to the formulation of the speed function. Most existing GDMs use a fixed form of speed function for all applications. They also don't explicitly take noise into consideration. In this paper, we address these problems by formalizing the meaning of speed function. We believe that the speed of interface evolution should be determined by the confidence (or lack of) that the interface is on the boundary of interest. We describe two new speed functions based on this concept and demonstrate their effectiveness with both simulated and actual medical data. Our results show that the new speed functions are less sensitive to noise, allow faster evolution, and provide a better stopping power.
Virtual Colonoscopy is a minimally invasive procedure to detect polyps in the colon using three dimensional tomographic imaging. An important step in analyzing the image data is accurate localization of the lumen surface. Polyps protruding into the colon lumen can then be identified by analyzing the surface either manually or using computer assisted techniques. We have developed a method for lumen segmentation based on a 3D geometric deformable model (GDM) to provide an improved representation of the lumen surface. The results show the GDM can remove the surface holes or tunnels that can be created with thresholding techniques and demonstrates the ability to accurately locate the lumen surface in high contrast air- tissue boundaries while preserving relatively lower contrast boundaries of the houstra and polyps.
Virtual colonoscopy is a minimally invasive technique that enables detection of colorectal polyps and cancer. Normally, a patient's bowel is prepared with colonic lavage and gas insufflation prior to computed tomography (CT) scanning. An important step for 3D analysis of the image volume is segmentation of the colon. The high-contrast gas/tissue interface that exists in the colon lumen makes segmentation of the majority of the colon relatively easy; however, two factors inhibit automatic segmentation of the entire colon. First, the colon is not the only gas-filled organ in the data volume: lungs, small bowel, and stomach also meet this criteria. User-defined seed points placed in the colon lumen have previously been required to spatially isolate only the colon. Second, portions of the colon lumen may be obstructed by peristalsis, large masses, and/or residual feces. These complicating factors require increased user interaction during the segmentation process to isolate additional colon segments. To automate the segmentation of the colon, we have developed a method to locate seed points and segment the gas-filled lumen with no user supervision. We have also developed an automated approach to improve lumen segmentation by digitally removing residual contrast-enhanced fluid resulting from a new bowel preparation that liquefies and opacifies any residual feces.