Biomedical subjects

Miles N Wernick

Publications and source records attributed to Miles N Wernick.

14 recordsLinked to original sources

PubMed2006

Computation of mass-density images from x-ray refraction-angle images.

In this paper, we investigate the possibility of computing quantitatively accurate images of mass density variations in soft tissue. This is a challenging task, because density variations in soft tissue, such as the breast, can be very subtle. Beginning from an image of refraction angle created by either diffraction-enhanced imaging (DEI) or multiple-image radiography (MIR), we estimate the mass-density image using a constrained least squares (CLS) method. The CLS algorithm yields accurate density estimates while effectively suppressing noise. Our method improves on an analytical method proposed by Hasnah et al (2005 Med. Phys. 32 549-52), which can produce significant artefacts when even a modest level of noise is present. We present a quantitative evaluation study to determine the accuracy with which mass density can be determined in the presence of noise. Based on computer simulations, we find that the mass-density estimation error can be as low as a few per cent for typical density variations found in the breast. Example images computed from less-noisy real data are also shown to illustrate the feasibility of the technique. We anticipate that density imaging may have application in assessment of water content of cartilage resulting from osteoarthritis, in evaluation of bone density, and in mammographic interpretation.

Absorptiometry, Photon↗

PubMed2006

Multiple-image radiography for human soft tissue.

Conventional radiography only provides a measure of the X-ray attenuation caused by an object; thus, it is insensitive to other inherent informative effects, such as refraction. Furthermore, conventional radiographs are degraded by X-ray scatter that can obscure important details of the object being imaged. The novel X-ray technology diffraction-enhanced imaging (DEI) has recently allowed the visualization of nearly scatter-free images displaying both attenuation and refraction properties. A new method termed multiple-image radiography (MIR) is a significant improvement over DEI, corrects errors in DEI, is more robust to noise and produces an additional image that is entirely new to medical imaging. This new image, which portrays ultra-small-angle X-ray scattering (USAXS) conveys the presence of microstructure in the object, thus differentiating homogeneous tissues from tissues that are irregular on a scale of micrometres. The aim of this study was to examine the use of MIR for evaluation of soft tissue, and in particular to conduct a preliminary investigation of the USAXS image, which has not previously been used in tissue imaging.

Arthrography↗

PubMed2006

A computed tomography implementation of multiple-image radiography.

Conventional x-ray computed tomography (CT) produces a single volumetric image that represents the spatially variant linear x-ray attenuation coefficient of an object. However, in many situations, differences in the x-ray attenuation properties of soft tissues are very small and difficult to measure in conventional x-ray imaging. In this work, we investigate an analyzer-based imaging method, called computed tomography multiple-image radiography (CT-MIR), which is a tomographic implementation of the recently proposed multiple-image radiography method. The CT-MIR method reconstructs concurrently three physical properties of the object. In addition to x-ray attenuation, CT-MIR produces volumetric images that represent the refraction and ultrasmall-angle scattering properties of the object. These three images can provide a rich description of the object's physical properties that are revealed by the probing x-ray beam. An imaging model for CT-MIR that is based on the x-ray transform of the object properties is established. The CT-MIR method is demonstrated by use of experimental data acquired at a synchroton radiation imaging beamline, and is compared to the pre-existing diffraction-enhanced imaging CT method. We also investigate the merit of an iterative reconstruction method for use with future clinical implementations of CT-MIR, which we anticipate would be photon limited.

Humans↗

PubMed2006

Methods to detect objects in photon-limited images.

We investigate the problem of detecting and localizing a known signal in a photon-limited image, where Poisson noise is the dominant source of image degradation. For this purpose we developed and evaluated three new algorithms. The first two are based on the impulse restoration (IR) principle and the third is based on the generalized likelihood ratio test (GLRT). In the IR approach, the problem is formulated as one of restoring a delta function at the location of the desired object. In the GLRT approach, which is a well-known variation on the optimal likelihood ratio test, the problem is formulated as a hypothesis testing problem, in which the unknown background intensity of the image and the intensity scale of the object are obtained by maximum-likelihood estimation. We used Monte Carlo simulations and localization receiver operating characteristic (LROC) curves to evaluate the proposed algorithms quantitatively. LROC curves demonstrate the ability of an algorithm to detect and locate objects in a scene correctly. Our simulations demonstrate that the GLRT approach is superior to all other tested algorithms.

Algorithms↗

PubMed2005

A physical model of multiple-image radiography.

We recently proposed a phase-sensitive x-ray imaging method called multiple-image radiography (MIR), which is an improvement on the diffraction-enhanced imaging technique. MIR simultaneously produces three images, depicting separately the effects of absorption, refraction and ultra-small-angle scattering of x-rays, and all three MIR images are virtually immune to degradation caused by scattering at higher angles. Although good results have been obtained using MIR, no quantitative model of the imaging process has yet been developed. In this paper, we present a theoretical prediction of the MIR image values in terms of fundamental physical properties of the object being imaged. We use radiative transport theory to model the beam propagation, and we model the object as a stratified medium containing discrete scattering particles. An important finding of our analysis is that the image values in all three MIR images are line integrals of various object parameters, which is an essential property for computed tomography to be achieved with conventional reconstruction methods. Our analysis also shows that MIR truly separates the effects of absorption, refraction and ultra-small-angle scattering for the case considered. We validate our analytical model using real and simulated imaging data.

Algorithms↗

PubMed2005

Relevance vector machine for automatic detection of clustered microcalcifications.

Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms.

Algorithms↗

PubMed2005

Spatiotemporal processing of gated cardiac SPECT images using deformable mesh modeling.

In this paper we present a spatiotemporal processing approach, based on deformable mesh modeling, for noise reduction in gated cardiac single-photon emission computed tomography images. Because of the partial volume effect (PVE), clinical cardiac-gated perfusion images exhibit a phenomenon known as brightening-the myocardium appears to become brighter as the heart wall thickens. Although brightening is an artifact, it serves as an important diagnostic feature for assessment of wall thickening in clinical practice. Our proposed processing algorithm aims to preserve this important diagnostic feature while reducing the noise level in the images. The proposed algorithm is based on the use of a deformable mesh for modeling the cardiac motion in a gated cardiac sequence, based on which the images are processed by smoothing along space-time trajectories of object points while taking into account the PVE. Our experiments demonstrate that the proposed algorithm can yield significantly more-accurate results than several existing methods.

Algorithms↗

PubMed2004

Tomographic image reconstruction based on a content-adaptive mesh model.

In this paper, we explore the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first represented by a mesh model, an efficient image description based on nonuniform sampling. In the CAMM, image samples (represented as mesh nodes) are placed most densely in image regions having fine detail. Tomographic image reconstruction in the mesh domain is performed by maximum-likelihood (ML) or maximum a posteriori (MAP) estimation of the nodal values from the measured data. A CAMM greatly reduces the number of unknown parameters to be determined, leading to improved image quality and reduced computation time. We demonstrated the method in our experiments using simulated gated single photon emission computed tomography (SPECT) cardiac-perfusion images. A channelized Hotelling observer (CHO) was used to evaluate the detectability of perfusion defects in the reconstructed images, a task-based measure of image quality. A minimum description length (MDL) criterion was also used to evaluate the effect of the representation size. In our application, both MDL and CHO suggested that the optimal number of mesh nodes is roughly five to seven times smaller than the number of projection bins. When compared to several commonly used methods for image reconstruction, the proposed approach achieved the best performance, in terms of defect detection and computation time. The research described in this paper establishes a foundation for future development of a (four-dimensional) space-time reconstruction framework for image sequences in which a built-in deformable mesh model is used to track the image motion.

Algorithms↗

PubMed2004

A similarity learning approach to content-based image retrieval: application to digital mammography.

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

Algorithms↗

PubMed2003

Multiple-image radiography.

Conventional radiography produces a single image of an object by measuring the attenuation of an x-ray beam passing through it. When imaging weakly absorbing tissues, x-ray attenuation may be a suboptimal signature of disease-related information. In this paper we describe a new phase-sensitive imaging method, called multiple-image radiography (MIR), which is an improvement on a prior technique called diffraction-enhanced imaging (DEI). This paper elaborates on our initial presentation of the idea in Wernick et al (2002 Proc. Int. Symp. Biomed. Imaging pp 129-32). MIR simultaneously produces several images from a set of measurements made with a single x-ray beam. Specifically, MIR yields three images depicting separately the effects of refraction, ultra-small-angle scatter and attenuation by the object. All three images have good contrast, in part because they are virtually immune from degradation due to scatter at higher angles. MIR also yields a very comprehensive object description, consisting of the angular intensity spectrum of a transmitted x-ray beam at every image pixel, within a narrow angular range. Our experiments are based on data acquired using a synchrotron light source; however, in preparation for more practical implementations using conventional x-ray sources, we develop and evaluate algorithms designed for Poisson noise, which is characteristic of photon-limited imaging. The results suggest that MIR is capable of operating at low photon count levels, therefore the method shows promise for use with conventional x-ray sources. The results also show that, in addition to producing new types of object descriptions, MIR produces substantially more accurate images than its predecessor, DEI. MIR results are shown in the form of planar images of a phantom and a biological specimen. A preliminary demonstration of the use of MIR for computed tomography is also presented.

Absorptiometry, Photon↗

PubMed2002

An evaluation of methods for detecting brain activations from functional neuroimages.

Brain activation studies based on PET or fMRI seek to explore neuroscience questions by statistically analyzing the acquired images to produce statistical parametric images (SPIs). An increasingly wide range of univariate and multivariate analysis techniques are used to generate SPIs in order to detect mean-signal activations and/or long-range spatial interactions. However, little is known about the comparative detection performance of even simple techniques in finite data sets. Our aims are (1) to empirically compare the detection performance of a range of techniques using simulations of a simple image phantom and receiver operating characteristics (ROC) analysis, and (2) to construct two near-optimal detectors, both generalized likelihood ratio tests as upper performance bounds. We found that for finite samples of (10-100) images, even when the t-test with single-voxel variance estimates (single-voxel t-test) is the "correct" (i.e. unbiased) model for simple local additive signals, better detection performance is obtained using pooled variance estimates or adaptive, multivariate covariance-based detectors. Normalization by voxel-based variance estimates causes significantly decreased detection performance using either single-voxel t-tests or correlation-coefficient thresholding compared to pooled-variance t-tests or covariance thresholding, respectively. Moreover, we found that SVD by itself, or followed by an adaptive Fisher linear discriminant, provides a detector that is (1) more sensitive to mean differences than a single-voxel t-test, (2) insensitive to the large local signal variances detected by covariance thresholding, and (3) much more sensitive to signal correlations than correlation-coefficient thresholding. Adaptive, multivariate covariance-based approaches and pooled-variance t-tests represent promising directions for obtaining optimal signal detection in functional neuroimaging studies.

Brain↗

PubMed2002

Digital camera system to perform infrared photography of iris transillumination.

The authors modified a standard digital camera by replacing an internal infrared-blocking filter that allowed photography of iris transillumination defects. Visible and infrared light was produced with a halogen fiberoptic light source that was placed against the inferior eyelid and directed perpendicular to the globe. To obtain images, the camera was mounted to a tabletop tripod placed 3 to 5 in from a chin/headrest apparatus. Images were downloaded onto a personal computer via software and accessories provided by the camera manufacturer. This technique, which is convenient and produces high quality, color images, can highlight details of the normal and pathologic iris. It may have significant utility in clinical research as well as routine clinical care.

Exfoliation Syndrome↗

PubMed2002

A support vector machine approach for detection of microcalcifications.

In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to out perform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.

Algorithms↗

PubMedDate not supplied

Simultaneous assessment of cardiac perfusion and function using 5-dimensional imaging with Tc-99m teboroxime.

BACKGROUND: Dynamic single photon emission computed tomography (SPECT) acquisition and reconstruction of early poststress technetium 99m teboroxime washout images has been shown to be useful in the detection of coronary disease. Assessment of poststress regional wall motion may offer additional use in assessing coronary disease. Our goal was to investigate the feasibility of simultaneously imaging myocardial ischemia and transient poststress akinesis using gated-dynamic SPECT. METHODS AND RESULTS: A gated-dynamic mathematical cardiac torso (MCAT) phantom was developed to model both teboroxime kinetics and cardiac regional wall motion. A lesion was simulated as having delayed poststress teboroxime washout together with a transient poststress wall motion abnormality. Gated projection data were created to represent a 3-headed SPECT system undergoing a total rotation of 480 degrees . The dynamic expectation-maximization reconstruction algorithm with postsmoothing across gating intervals by Wiener filtering, and the ordered-subset expectation maximization reconstruction algorithm with 3-point smoothing across gating intervals were compared. Compared with the ordered-subset expectation maximization with 3-point smoothing, the dynamic expectation-maximization algorithm with Wiener filtering was able to produce visually higher-quality images and more accurate left ventricular ejection fraction estimates. CONCLUSION: From simulations, we conclude that changing cardiac function and tracer localization possibly can be assessed by using a gated-dynamic acquisition protocol combined with a 5-dimensional reconstruction strategy.

Algorithms↗