Biomedical subjects

Ross T Whitaker

Publications and source records attributed to Ross T Whitaker.

7 recordsLinked to original sources

PubMed2006

Spatial distribution and orientation of dermatan sulfate in human medial collateral ligament.

The proteoglycan decorin and its associated glycosaminoglycan (GAG), dermatan sulfate (DS), regulate collagen fibril formation, control fibril diameter, and have been suggested to contribute to the mechanical stability and material properties of connective tissues. The spatial distribution and orientation of DS within the tissue are relevant to these mechanical roles, but measurements of length and orientation from 2D transmission electron microscopy (TEM) are prone to errors from projection. The objectives of this study were to construct a 3D geometric model of DS GAGs and collagen fibrils, and to use the model to interpret TEM measurements of the spatial orientation and length of DS GAGs in the medial collateral ligament of the human knee. DS was distinguished from other sulfated GAGs by treating tissue with chondroitinase B, an enzyme that selectively degrades DS. An image processing pipeline was developed to analyze the TEM micrographs. The 3D model of collagen and GAGs quantified the projection error in the 2D TEM measurements. Model predictions of 3D GAG orientation were highly sensitive to the assumed GAG length distribution, with the baseline input distribution of 69+/-23 nm providing the best predictions of the angle measurements from TEM micrographs. The corresponding orientation distribution for DS GAGs was maximal at orientations orthogonal to the collagen fibrils, tapering to near zero with axial alignment. Sulfated GAGs that remained after chondroitinase B treatment were preferentially aligned along the collagen fibril. DS therefore appears more likely to bridge the interfibrillar gap than non-DS GAGs. In addition to providing quantitative data for DS GAG length and orientation in the human MCL, this study demonstrates how a 3D geometric model can be used to provide a priori information for interpretation of geometric measurements from 2D micrographs.

Chondroitinases and Chondroitin Lyases↗

PubMed2006

Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification.

This paper presents a novel method for brain-tissue classification in magnetic resonance (MR) images that relies on a very general, adaptive statistical model of image neighborhoods. The method models MR-tissue intensities as derived from stationary random fields. It models the associated Markov statistics nonparametrically via a data-driven strategy. This paper describes the essential theoretical aspects underpinning adaptive, nonparametric Markov modeling and the theory behind the consistency of such a model. This general formulation enables the method to easily adapt to various kinds of MR images and the associated acquisition artifacts. It implicitly accounts for the intensity nonuniformity and performs reasonably well on T1-weighted MR data without nonuniformity correction. The method minimizes an information-theoretic metric on the probability density functions associated with image neighborhoods to produce an optimal classification. It automatically tunes its important internal parameters based on the information content of the data. Combined with an atlas-based initialization, it is completely automatic. Experiments on real, simulated, and multimodal data demonstrate the advantages of the method over the current state-of-the-art.

Algorithms↗

PubMed2006

Unsupervised, information-theoretic, adaptive image filtering for image restoration.

Image restoration is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Hence, these methods lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsupervised, information-theoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing their joint entropy. In this way, UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images. The paper describes the formulation to minimize the joint entropy measure and presents several important practical considerations in estimating neighborhood statistics. It presents a series of results on both real and synthetic data along with comparisons with current state-of-the-art techniques, including novel applications to medical image processing.

Algorithms↗

PubMed2005

MRI tissue classification with neighborhood statistics: a nonparametric, entropy-minimizing approach.

We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric density estimation. The method models images as random fields and relies on minimizing an entropy-based metric defined on high dimensional probability density functions. Combined with an atlas-based initialization, it is completely automatic. Experiments on real and simulated data demonstrate the advantages of the method in comparison to other approaches.

Algorithms↗

PubMed2005

Case study: an evaluation of user-assisted hierarchical watershed segmentation.

This paper evaluates the effectiveness of an interactive, three-dimensional image segmentation technique that relies on watersheds. This paper presents two user-based case studies, which include two different groups of domain experts. Subjects manipulate a graphics-based front end to a hierarchy of segmented regions generated from a watershed segmentation algorithm, which is implemented in the Insight Toolkit. In the first study, medical students segment several different anatomical structures from the Visible Human Female head and neck color cryosection data. In the second study, radiologists use the interactive tool to produce models of brain tumors from MRI data. This paper presents a quantitative and qualitative comparison against hand contouring. To quantify accuracy, we estimate ground truth from the hand-contouring data using the Simultaneous Truth and Performance Estimation algorithm. We also apply metrics from the literature to estimate precision and efficiency. The watershed segmentation technique showed improved subject interaction times and increased inter-subject precision over hand contouring, with quality that is visually and statistically comparable. The analysis also identifies some failures in the watershed technique, where edges were poorly defined in the data, and note a trend in the hand-contouring results toward systematically larger segmentations, which raises questions about the wisdom of using expert segmentations to define ground truth.

Algorithms↗

PubMed2004

GIST: an interactive, GPU-based level set segmentation tool for 3D medical images.

While level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. The second problem is compounded by the first. This paper describes a new tool for 3D segmentation that addresses these problems by computing level-set surface models at interactive rates. This tool employs two important, novel technologies. First is the mapping of a 3D level-set solver onto a commodity graphics card (GPU). This mapping relies on a novel mechanism for GPU memory management. The interactive rates level-set PDE solver give the user immediate feedback on the parameter settings, and thus users can tune free parameters and control the shape of the model in real time. The second technology is the use of intensity-based speed functions, which allow a user to quickly and intuitively specify the behavior of the deformable model. We have found that the combination of these interactive tools enables users to produce good, reliable segmentations. To support this observation, this paper presents qualitative results from several different datasets as well as a quantitative evaluation from a study of brain tumor segmentations.

Algorithms↗

PubMed2002

A direct approach to estimating surfaces in tomographic data.

Under ideal circumstances, the inverse of the radon transform is computable, and sequences of measured projections are sufficient to obtain accurate estimates of volume densities. In situations where the sinogram data is incomplete, the radon transform is noninvertable, and attempts to reconstruct greyscale density values result in reconstruction artifacts that can undermine the effectiveness of subsequent processing. This paper presents a direct approach to the segmentation of incomplete tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the measured sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Relative to previous work, this paper makes several contributions. First is a proper derivation of the deformation of surface models that move according to a gradient descent on a likelihood measure. We also present a series of computational innovations that make this direct surface-fitting approach feasible with state-of-the-art computers. Another contribution is the demonstration of the effectiveness of this approach on under-constrained tomographic problems, using both simulated and real datasets.

Algorithms↗