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To quantify the complex relationships between (1) the temperature, and temperature differences, on the surface of the breast as recorded by infrared thermal imaging and (2) the underlying physiological and pathological factors, we have developed a dynamic finite element method for comprehensive modeling of both the thermal and elastic properties of normal and tumorous breast tissues. In the steady state, the gravity-induced deformation is found to cause markedly asymmetric surface temperatures even though all thermal-elastic properties are symmetrical. In the dynamic state, the time course of breast thermal imaging in cold-stress and thermal-recovery procedures is found to be useful in characterizing the origins of the thermal contrast on the breast surface. The tumor-induced thermal contrast has slower temporal behavior than the deformation-induced thermal contrast on the breast surface, which may lead to improvements in breast-tumor diagnosis.

The probabilistic distribution properties of a set of medical images are studied. It is shown that the generalized Gaussian function provides a good approximation to the distribution of AP chest radiographs. Based on this result and a goodness-of-fit test, a generalized Gaussian autoregressive model (GGAR) is proposed. Its properties and limitations are also discussed. It is expected that the GGAR model will be useful in describing the stochastic characteristics of some classes of medical images and in image data compression and other applications. >

We report on the reproducibility of human observers' vanishing detection thresholds for visual targets in contrast-detail (C/D) analysis of ultrasound B-mode images. The images used in this study contain visual targets which are circular cross sections of constant-contrast conical structures in the C/D phantom. The vanishing threshold diameters for these targets vary as a function of the perceived size of the imaged target, target-to-background contrast, image noise content, and reproducibility of the decision levels of human observers for repeated observations. Our study indicates that the determination of absolute vanishing threshold diameter values for several targets of different contrast by human observers yields a high degree of error that is not predicted by existing theoretical assumptions based on a static threshold detector. We find that systematic error is introduced by the observers during the course of the experiment and that the levels of sensitivity of the observers differ widely at all times, and increase the amount of total observer error. These results suggest that, due to the large total observer error, C/D analysis may be impractical in a clinical environment, unless there is access to a team of observers specifically and extensively trained in this task. We suggest that a computer-based observer may be more reliable for the objective performance of contrast-detail analysis as a method for evaluating ultrasound image quality and comparison of imaging systems.

An evolutionary algorithm (ES) for automated deconvolution of γ-ray spectra is described that fits peak shape morphologies typical of spectra acquired from variably radiation damaged γ-ray detectors. Space radiation effects significantly impair semi-conductor γ-ray detector efficiency and induce variable degrees of nuclide peak broadening, distortion in spectra. Mars Odyssey Gamma-ray spectrometer data are used to demonstrate applicability of described algorithms for three degrees of radiation damage. ES methods accurately identify and quantify the discrete set of nuclide peaks in an arbitrary spectrum using a nuclide library. A novel method of constraining peak low energy tails, broadened by detector radiation damage, reduces the peak shape model from six parameters to four yielding a significant minimization of model complexity. Benefits of this approach include the simple implementation of highly specific parameter constraints that appropriately define feasible solution spaces. Methods describe peak low energy tailing descriptors as a continuum of low energy peak tailing curves representing increasing degrees of radiation damage. Curves are addressable by a single real valued parameter. Results illustrate the use of methods to simply describe relative radiation dosimetry using this parameter. Analysis of degraded spectra indicates method sensitivity to low and high levels of space radiation damage prior to and post MO-GRS detector annealings.

One aim of this work is to investigate the feasibility of using a hierarchy of models to describe diffusion tensor MRI data. Parsimonious model selection criteria are used to choose among different models of diffusion within tissue. Second, based on this information, we assess whether we can perform simultaneous tissue segmentation and classification. The proposed hierarchical framework used for parsimonious model selection is based on the F-test, adapted from Snedecor. Diffusion Magnetic Resonance Microscopy (MRM) provides near-microscopic resolution without relying on a sample's optical transparency for image formation. Diffusion MRM is a noninvasive imaging technique for quantitative analysis of intrinsic features of tissues. Thus, we propose using Diffusion MRM to characterize normal tissue structure in adult zebrafish, and possibly subtle anatomical or structural differences between normals and knockouts. Both numerical phantoms and diffusion weighted image (DWI) data obtained from adult zebrafish are used to test this model selection framework.

Model observers have been used successfully to predict human observer performance and to evaluate image quality for detection tasks on various backgrounds in medical applications. This paper will apply the closed-form compression noise statistics in analytic form to model observers and the derived channelized Hotelling observer (CHO) for decompressed images. The performance of CHO on decompressed images is validated using JPEG compression algorithm and lumpy background images. The results show that the derived CHO performance predicts closely its simulated performance.

The lining of the bladder is comprised of well defined layers that are clearly visible in optical coherence tomography (OCT) images of healthy bladder tissue. These layers are disturbed when cancerous cells are present. Consequently, recognition and classification of the layers of the bladder is very important in recognizing and staging bladder cancer. We present an algorithm that uses texture analysis, the k-means clustering algorithm, and edge detection software to segment OCT images of the lining of the bladder. We visually segmented 101 OCT images of bladder tissue, ran our algorithm on the images, and compared the results. The segmentation matched 59% of the time, matched reasonably well, with clear sources of error 29% of the time, and was incorrect 12% of the time. The combination of this algorithm with future texture analysis of the segmented layers will provide the tools required to perform a real-time optical biopsy using OCT

Two types of thermoluminescence dosemeters (TLDs), the Harshaw LiF:Mg,Ti (TLD-100) and CaF(2):Tm (TLD-300) were investigated for their glow curve response to separate photon and proton irradiations. The TLDs were exposed to gamma irradiation from a (137)Cs source and proton irradiation using a positive ion accelerator. The glow curve peak structure for each individual TLD exposure was deconvolved to obtain peak height, width, and position. Simulated mixed-field glow curves were obtained by superposition of the experimentally obtained single field exposures. Feature vectors were composed of two kinds of features: those from deconvolution and those taken in the neighbourhood of several glow curve peaks. The inner product of the feature vectors was used to discriminate among the pure photon, pure proton and simulated mixed-field irradiations. In the pure cases, identification of radiation types is both straightforward and effective. Mixed-field discrimination did not succeed using deconvolution features, but the peak-neighbourhood features proved to discriminate reliably.

Compression methods based on Gabor functions are implemented for simulated nuclear medicine liver images with and without lesions. The performance of the compression schemes is assessed objectively by comparing the original images to the compressed/reconstructed images through calculation of the Hotelling trace, an index that has been shown to correlate well with performance for images from this imaging modality. Gabor-based compression has not previously been implemented on medical images, nor has any rigorous task-based measure of quality been used to assess the compression. For compression based on thresholding the complex Gabor coefficients, a better than 2∶1 compression is obtained without appreciable reduction in image quality, which when combined with gains expected from bit reduction schemes, corresponds to an overall approximate 8∶1 compression. A large number of nuclear medicine liver images with and without space-occupying lesions were simulated. Then a compression scheme based on transformation of the images into the “information space” proposed by Gabor [1] was implemented. Two tasks were examined: 1) determination of the presence or absence of the lesion in a given location, and 2) determination of the presence or absence of the lesion in one of several locations. The task-based performance using the compressed/reconstructed images is compared to that using the original images according to the Hotelling trace criterion.

In this paper, correlation properties of a class of chest X-ray medical images are examined and different 1-D and 2-D correlation models are applied to this class of image sources. Correlation structures for different scanning methods including row-by-row, column-by-column, diagonal and Peano are compared. It is suggested that the Peano scanning best reserves the inter-pixel correlation, which coincides with an earlier observation made by Lempel and Ziv. l.N The correlation function defines the intemal structure of the inter-dependence of image pixels and forms an important aspect of medical image characteristics. It is crucial for designing a data compression scheme, especially for transform coding and linear predictive coding schemes[JAIN 8 l][CLAR 851. Many researches have been conducted in this area and many correlation models have been proposed. These include Markov, separable, isotropic, generalized correlation models [NATA 781 [MAUE 79][CLAR 851. They have also been extensively applied to image coding and other processing applications for different types of images. The study of correlation characteristics for medical images is, however, very limited. In this paper, correlation properties of a class of chest X-ray medical images are examined and different 1-D and 2-D correlation models are applied to this class of image sources. Correlation structures for different scanning methods including row-by-row, column-bycolumn, diagonal and Peano are compared. It is suggested that the Peano scanning best reserves the inter-pixel correlation, which coincides with an earlier observation made by Lempel and Ziv [ZIV 861.

When a patient is examined at different times using different protocols, how can we know whether the observed differences in the area or volume estimate are due to the patient, the protocol, or both? Specifically, we ask what is the smallest difference in lung volume that can be computed reliably when two sets of CT data are acquired by varying the number and thickness of the slices, but while holding constant the in-plane resolution. The accuracy and precision of the total lung volume estimates are calculated based on the principles of stereology using uniform design sampling. Comparisons of the lung volume estimate based on fewer slices using stereological principles are employed. A formal test made of the hypothesis that the use of fewer slices can yield satisfactory precision of the lung estimate. It is known that estimation of lung volume based on CT images is sensitive to the acquisition parameters used during scanning: dose, scan time, number of cross-sectional slices, and slice collimation. Those parameters are very different depending on the lung examination required: routine studies or high-resolution detailed studies. Thus, if different protocols are to be used confidently for volume estimation, it is important to understand the factors that influence volume estimate accuracy and to provide the associated confidence intervals for the measurements.

The most common metric to assess a classifier's performance is the classification error rate, or the probability of misclassification (PMC). Receiver operating characteristic (ROC) analysis is a more general way to measure the performance. Some metrics that summarize the ROC curve are the two normal-deviate-axes parameters, i.e., a and b, and the area under the curve (AUC). The parameters "a" and "b" represent the intercept and slope, respectively, for the ROC curve if plotted on normal-deviate-axes scale. AUC represents the average of the classifier TPF over FPF resulting from considering different threshold values. In the present work, we used Monte-Carlo simulations to compare different bootstrap-based estimators, e.g., leave-one-out, .632, and .632+ bootstraps, to estimate the AUC. The results show the comparable performance of the different estimators in terms of RMS, while the .632+ is the least biased.

Increased blood glucose stimulates pancreatic β-cells and induces an exocytotic release of insulin. The β-cell, which contains ~10^4 insulin-containing granules, releases only a few percent of the granules during a given stimulus such as a meal. The temporal response function to a square wave increase in the concentration of glucose is characteristically biphasic. It is not known whether the granules exhibit random or directed migration patterns as a function of phase. Directed migration would suggest the development of an intracellular gradient directing the path and velocity of insulin granule movement. Our ongoing research investigates this process using manual morphometric analysis of electron micrographs of rat pancreatic β-cells. This is a tedious and time-consuming stereological process. Consequently, we have developed an automated algorithm for accurately segmenting and deriving granule counts, areas, and measuring distance to the plasma membrane. The method is a data-driven image processing approach that implements Mahalanobis classifiers to hierarchically classify pixel candidates and subsequently pixel aggregates as insulin granules. Granule cores and halos are classified independently and fused by intersecting the convex difference of granule halos with core candidates. Once fused, total and individual granule areas and distance metrics to the β-cell plasma membrane are obtained. This algorithm provides a rapid and accurate method for the determination of granule numbers, location, and potential gradients in the pancreatic β-cell under different experimental conditions.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper provides the fundamental basis for model observers on decompressed images by the understanding of compression noise statistics. In medical applications, model observers have been successfully used to predict human observer performance and to empirically evaluate image quality for detection tasks on various backgrounds. To derive closed-form expressions for model observers, however, requires closed-form expressions for noise statistics. This paper views a decompressed image as the sum of the original image and compression noise. The statistics of compression noise depend on the compression algorithms. One of the most efficient image compression techniques is transform coding, on which the JPEG image compression standard is based. By analyzing transform coding, this paper derives probability density functions (PDF), and the first and second moments of compression noise. Those statistics are used to derive closed-form representations for the ideal and channelized Hotelling observers on decompressed images. It provides the closed-form decompressed image quality measurements in terms of model observer performance.

This paper studies the principle of transform coding and identifies the quantization noise as the sole distortion. It shows that compression noise is a linear transform of quantization noise, which is usually generated during quantization of transform coefficients using uniform scalar quantizers. The quantization noise may not distribute uniformly as distributions and quantization step sizes vary among transform coefficients. This paper derives the marginal, pairwise and joint probability density functions (pdfs) of multi-dimensional quantization noise. It also shows the mean vector and covariance matrix of quantization noise in closed-form. Based on above results, this paper derives closed-form compression noise statistics, which include marginal pdfs, pairwise pdfs and joint pdf, mean vector and covariance matrix of compression noise. This paper shows compression noise has a jointly normal distribution, which enables its calculation to have reasonable computation complexity. The derived statistics of quantization and compression noise are verified by using the JPEG compression algorithm and lumpy background images. Verification results show that derived statistics closely predicts estimated ones. This paper provides a theoretical foundation to derive closed-form model observers and to define closed-form quality measures for compressed medical images.

We derive a general random-effects model to study the differences in spatial frequency features across class-type (true positive (TP) or true negative (TN) or False Positive (FP)), a sample of 40 mammogram cases, and 9 readers. We derive a measure of feature conspicuity or salience using visually inspired spatial frequency filters and mammogram regions of interest derived from eye-position data. Repeated-measures ANOVA is performed on the salient features obtained from all cases. We hypothesize that statistically significant differences in the average salience measure (D-score) are seen across both class-types and cases. We believe this to be useful for determining the similarity between images in training and testing sets used in CADx algorithm development or for a priori determination of test set difficulty. Further, we hypothesize that our salience measure is useful for distinguishing the spatial frequency bands most relied upon to distinguish true negative and true positive responses. This is useful in discerning the "bottoms-up" cues used to direct the point of gaze during mammogram inspection. These results indicate that our salience measure is useful as an indicator of image similarity and for separating TP & TN regions of interest.

The US Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate (NVESD) has a dynamic applied research program in sensor fusion for a wide variety of defense & defense related applications. This paper highlights efforts under the NVESD Sensor Fusion Testbed (SFTB) in the area of detection of moving vehicles with a network of image and acoustic sensors. A sensor data collection was designed and conducted using a variety of vehicles. Data from this collection included signature data of the vehicles as well as moving scenarios. Sensor fusion for detection and classification is performed at both the sensor level and the feature level, providing a basis for making tradeoffs between performance desired and resources required. Several classifier types are examined (parametric, nonparametric, learning). The combination of their decisions is used to make the final decision.

As digital imaging technology advances, the amount of image data we generate increases and the need for compressed images becomes apparent. Because lossy compression will yield higher compression ratios than lossless methods, objective assessment metrics of reconstructed image quality are needed. In medical applications, model observers, especially the channelized Hotelling observer, have been successfully used to predict human observer performance and to evaluate image quality for detection tasks on various backgrounds. To use model observers, however, requires knowledge of noise statistics. This paper finds closed-form expressions for the noise induced by transform coding, one of the most commonly used methods for image compression. Knowledge of the noise enables us to study the effect of image compression on the clinical utility of medical images that have been reconstructed after being compressed using transform coding. In this paper, by analyzing image compression procedures, we propose a block-based transform coding representation in 1-D form, identify the quantization noise as the sole distortion source in transform coding, and derive the compression noise statistics. We show that the probability density function (pdf) of the compression noise is defined as a function of the transform matrix and its corresponding quantization matrix in the transform coding algorithm. We prove that the compression noise is a normal distribution when the dimension of the transform (the block size) is typical. We also provide the pdf of JPEG compression noise as a function of the quantization table and the DCT transform bases. This work provides the theoretical foundation for using the model observers in closed mathematical form, and can be applied to other image compression application areas that require the statistics of compression noise as well.

Estimation of volume or the ratio of volumes in an image requires both mensuration and classification. The former is achieved through stereology - a set of techniques that estimate such parameters as area, volume, surface area, length, and number. Classification is achieved by extracting features that capture discriminating information (e.g., about tissue type). Both stereology and classification can be performed either manually or by computer. Manual techniques for the combination are based on coarse point counting (low resolution), but assumed perfect pixel classification. Computer-based methods, on the other hand, rely on very fine point counting but in general suffer from imperfect pixel classification. This paper examines the interaction between manual and image processing-based approaches; in particular, we present a measure that combines the classification and measurement errors. Estimation of the variance is used to define the conditions under which each method is and is not advantageous despite its underlying error. This allows the user to choose a method that optimizes overall performance, given the human and machine capabilities available. Illustrations are given of cases in which each method can be preferable, as measured by the variance of the estimate of the performance that was inferred from the measurement.

We present a method for locating salient features in mammograms. The perceptual importance or salience of image pixels can be studied using a statistical measure of pixel-based features. The “outliers” or greatest values for this measure can be regarded as salient because in an imaging sense, the outliers tend to contribute to the local feature contrast. Our method finds important image features first by spatially decomposing the image using a process that models the human vision system. Salience maps then are created using the Mahalanobis distance, and a scalar visibility metric then is analyzed. Six mammographers each read three mammograms. Each mammogram had two views. During screening, eye position data were recorded. A K-means algorithm then was applied to identify fixation clusters. Following decomposition, Analysis of Variance (ANOVA) then was performed to examine the effects of observer experience, spatial frequency, and discrimination using the maximum value of the visibility metric. This pilot study shows statistically significant differences in true positive and true negative features, and in both the features and filters used to discriminate true negative results between expert and resident observers. This type of analysis can be useful for finding fixation tendencies that result from the available spatial features during mammogram screening.

This paper provides a theoretical foundation for the closed-form expression of model observers on compressed images. In medical applications, model observers, especially the channelized Hotelling observer, have been successfully used to predict human observer performance and to evaluate image quality for detection tasks in various backgrounds. To use model observers, however, requires knowledge of noise statistics. This paper first identifies quantization noise as the sole distortion source in transform coding, one of the most commonly used methods for image compression. Then, it represents transform coding as a 1-D block-based matrix expression, it further derives first and second moments, and the probability density function (pdf) of the compression noise at pixel, block and image levels. The compression noise statistics depend on the transform matrix and the quantization matrix in the transform coding algorithm. Compression noise is jointly normally distributed when the dimension of the transform (the block size) is typical and the contents of image sets vary randomly. Moreover, this paper uses JPEG as a test example to verify the derived statistics. The test simulation results show that the closed-form expression of JPEG quantization and compression noise statistics correctly predicts the estimated ones from actual images.

In many cases the combined assessment of three-dimensional anatomical and functional images [single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT)] is necessary to determine the precise nature and extent of lesions. It is important, prior to performing the addition, subtraction, or any other combination of the images, that they be adequately aligned and registered either by experienced radiologists via visual inspection, mental reorientation and overlap of slices, or by an automated registration algorithm. To be useful clinically, the latter case requires validation. The human capacity to evaluate registration results visually is limited and time consuming. This paper describes an algorithmic procedure to provide proxy measures for human assessment that discriminate between badly misregistered pairs of brain images and those likely to be clinically useful. The new algorithm consists of four major steps: segmentation of brain and skin/air boundaries, contour extraction, computation of the principal axes, and computation of the registration quality measures from the contour volumes. The test data were MR and CT brain images. The results of the present study indicate that the use of a measure based on the combination of brain and skin contours and a principal axis function is a good first step to reduce the number of badly registered images reaching the clinician.

An efficient subband implementation of the channelized Hotelling observer is presented, which can be used to assess the image quality of images compressed with wavelet-based techniques. The channelized Hotelling model observer has been shown to predict human performance in detecting signals in noise-limited images. The model observer can also predict degradation of human performance due to lossy compressed images. This provides a more relevant image quality assessment for medical images, where the image's value is in supporting clinical decisions, than metrics such as mean square error. The subband implementation shown is unique in that it operates on channel responses of the wavelet subbands rather than on the entire image itself. The technique is extendable to operate on the channel response of the subband bitmaps, which would permit bit ordering optimized for human performance.

Estimation of volume or area of tissue types in an image requires both mensuration and classification. The former is achieved through stereology -- a set of techniques that estimate such parameters as area, volume, surface area, length, and number. Classification is achieved by extracting features that capture the discriminating information about tissue type. Both stereology and classification can be performed either manually or by computer. Manual techniques for the combination are based on coarse point counting (low resolution), but assumed perfect pixel classification. Computer-based methods, on the other hand, rely on very fine point counting but in general suffer from imperfect pixel classification. This paper examines the interaction between manual and image processing-based approaches; in particular, we present a measure that combines the classification and measurement errors. Estimation of the variance is used to define the conditions under which each method is and is not advantageous despite its underlying error. This allows the user to choose a method that optimizes overall performance, given the human and machine capabilities available. Illustrations are given of cases in which each method can be preferable, as measured by the variance of the estimate of the performance that was inferred from the measurement.

A generalized Gaussian autoregressive (GGAr) model is proposed, and its properties and limitations are discussed. Some mathematical difficulties were encountered with the model, caused by the fact that the probability density distribution of the prediction error can not be expressed in closed form. Nevertheless, the GGAr model better describes the stochastic characteristics of medical X-ray images and is expected to be useful in image data compression and other applications. >

A method is proposed for constructing the joint probability model from a specified first-order distribution and correlation structure. The approach is based on an invertible nonlinear transformation. The model's information-theoretic properties (entropy, rate-distortion bound) are examined. The results can be used to analyze and evaluate the performance of an image data compression system. It is believed that this class of model and its information-theoretic properties may make for more realistic modeling of medical images and hence their more efficient processing. >

The US Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate has a dynamic applied research program in sensor fusion for a wide variety of defense & defense related applications. This paper provides an overview of the on going research at NVESD related to fusing a mixture of active and passive sensors for countermine, dismounted & mounted soldiers, aviation and unattended ground sensor applications. Highlighted are new techniques in image registration and sensor fusion ion that enable the detection of moving vehicles with a network of image and acoustic sensors. A set of experiments was designed and conducted using a variety of vehicles and scenarios. The incremental value (in terms of error probability) added to the acoustic information by the image sensors (visible and infrared wavelength cameras) is assessed in combination with the fusion techniques themselves. The approach specifically accounts for the effects of location, speed, weather, and background (acoustic, visible, and infrared). Sensor fusion for detection and classification is preformed at both the sensor level and the feature level providing a basis for making tradeoffs between performance desired and resources required. Several classifier types are examined (parametric, nonparametric, learning). The combination of their decisions is used to make the final decision. Keywords: Tracking, fusion, sensor fusion, vehicle detection, detection, location.

Fractal dimension (fd) is a feature which is widely used to characterize medical images. Previously, researchers have shown that fd separates important classes of images and is orthogonal to other texture measures. Existing methods of estimating fd, however, are unable to distinguish many images with similar characteristics, limiting the effectiveness of fd as a feature. This paper analyzes the limitation of box-counting (BC) and fractional Brownian motion (fBm) methods of estimating fd. Estimation by BC is ineffective when applied to data-limited images. Estimation by fBm uses a restrictive model which is not applicable to Iterated Function Systems or to noisy images. The authors present a new method of estimating fd which is applicable to data-limited medical images. In the authors' method, Fractal Interpolation Functions (FIFs) are used to generate self-affine models of graphical representations of image data. The fd of each FIF is analytically evaluated, and measures of confidence and self-affinity are computed. The new method is demonstrated in a test study of red blood cells.

A median filtered pyramidal multiresolution (MFPM) image segmentation method for detecting and delineating compact objects was applied to single-photon emission computed tomography (SPECT) images. These SPECT images were obtained by scanning spheres from 1 to 54 ml in size and from 50% to 100% contrast settings. The algorithm performed accurately for large sphere (20 and 54 ml) and high contrast (90% and 100%) cases. As the size and contrast decrease, the accuracy of the method also decreases. Comparison of the MFPM method with adaptive thresholding with edge-preserving smoothing (ATEPS) indicated superior performance of the MFPM method. >

To compress medical images in a lossy way that preserves their diagnostic value, the authors have combined lossless compression techniques with a fractal image compression method using two kinds of iterated function systems: partitioned, and condensation-model. The algorithm appears to yield a compression ratio of about 15:1 without perceptible degradation. >

In radionuclide therapy, absorbed dose is calculated by convolution of a three-dimensional activity matrix with a three-dimensional dose point kernel. A technique employing the fast Hartley Transform (FHT) has been developed to perform this calculation. An important part of that development was the indexing scheme for 3D data. The results of this new FHT convolution technique were compared to direct convolution. A cube was convolved with itself by these two techniques. The results differed by less than 2 percent. In an effort to show the practical applicability of 3D convolution, a three-dimensional activity matrix from a I-131-labeled 16.88 monoclonal antibody patient was convolved with beta and photon dose point kernels using direct convolution. Isodose contours were then generated from the calculated absorbed dose matrix and overlaid on a CT image of the patient. >

An approach for the application of multivariate pattern recognition techniques to detection of diffuse and focal disease using acoustic data is reviewed. Supervised and unsupervised techniques are implemented to design the best ultrasonic tissue signature for a given task from a set of measurements. The performances of both techniques are evaluated and compared using several methods. However, it is desirable to utilize a technique that quantitatively detects and displays the heterogeneity of an ultrasound image. It is shown that, for a particular task, choosing features with physical significance will make the classification of the data more robust. It is also shown that the success of combining supervised and unsupervised techniques using such features extends beyond discrimination of one class of data from the other and that the approach can be used to grade and the variations in the same tissue type. >

A medical virtual instrument has been developed to acquire and analyze the phonocardiogram (PCG), the heart sound and murmur signal. This instrument consists of a Pentium 200 computer running Windows95, equipped with LabVIEW software and a plug-in data acquisition board, and a two-channel custom designed bio-signal preamplifier. The bio-signal preamplifier allows the data acquisition board to acquire both the PCG and the electrocardiogram (ECG). LabVIEW software modules have been developed to create virtual instrument's front panels for the following functions: to manage patient information and data files, to capture and display current ECG and heart sound signals while saving or analyzing previous acquired signals, to perform the spectral and time-frequency analysis of the heart sounds and murmurs, and to review the previous recordings. This instrument can be used to display the PCG and to analyze the individual heart sound and murmur for the detection of heart valve diseases. It can also be used to analyze the carotid bruit for the diagnosis of carotid artery stenosis. This study demonstrated that a LabVIEW-based medical virtual instrument provides a low-cost, reliable, and flexible solution for data acquisition and analysis of the PCG and carotid bruit.

In this preliminary study the authors analyzed eyetrack data that was recorded while radiologists reviewed mammography images. The method of collecting the eyetrack data has been previously described by Barrett et al. (Acad. Radiol., vol. 1, p.40-5, 1994). The goal of this effort was to identify areas of microcalcifications from the eyetrack information. The identification of areas of microcalcification was accomplished using a Bayes classifier trained on images for which the presence of microcalcifications was verified by biopsies.

Optical methods for detecting tumors in tissue are desirable in part because optical photons are non-ionizing. The highly scattering nature of tissue makes traditional, and even time-gated, optical imaging impractical for sites deeper than a few millimeters. Scattering in tissue causes dispersion in the path lengths of traversing photons which blurs images. With a theoretical description of path length dispersion, however, the perturbation caused by a localized anomaly may be identified from time-resolved intensity data. Random walk theory has been used to show a quantitative, closed-form relationship between the perturbation and the inclusion size and scattering properties relative to the background. Using Monte Carlo data, the authors show a method for analyzing the sensitivity of the relationship to signatures of small, deeply embedded, abnormally scattering inclusions.

Ultrasound is an attractive diagnostic medical imaging modality because of its safety, speed, relative low cost, and versatility. It is used successfully in imaging of shape, structure, and size in two and three dimensions; in the measurement of blood flow, and in quantitative characterization of tissue. The paper examines some of the models and processing techniques that make those applications possible. For example, quantitative ultrasound tissue characterization requires the estimation of parameters from the observed ultrasound radio-frequency signal. First, those parameters must be identified, second, algorithms used to extract those parameters from the observed signal must be developed; third, the classifier must be chosen; and finally, those extracted parameters are used to train the classifier, which is then tested to estimate classification performance. The methods are applied to the imaging of several kinds of liver tissue.

It is commonly thought that improvements in image S/N translate directly to improvements in fMRI sensitivity. This study demonstrates that improvements in image S/N by means of increased field strength, and the use of surface coils, does not translate to similar gains in temporal S/N, due to physiological noise. An analysis of the S/N dependence on signal strength for both the imaginary and real noise components is presented. The real noise component, which is lower than the imaginary component, is a significant contributor to temporal variations in single-shot fMRI procedures. The imaginary component becomes an important contributor to time series fluctuations in multi-shot or 3D techniques. In the experiments presented here, the relationship between image and temporal S/N is examined by modulating the signal strength by means of echo time stepping, field strength modulation, and RF coil comparison. The spatial and temporal noise contributions of the resting brain are characterized by comparing phantom, subject, and thermal noise measurements. The sensitivity of both spiral and EPI single-shot acquisition methods to physiologic and systematic noise is characterized. The results suggest that the fMRI sensitivity plateaus as image S/N is increased unless physiological noise is filtered out.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The objective of this study is to develop a phonocardiogram (PCG) acquisition and analysis instrument using virtual instrumentation technology and investigate its Internet-based application. The PCG instrument was developed using a Pentium 200 computer, a data acquisition board, and a two-channel custom designed bio-signal preamplifier. LabVIEW was used to create the instrument's front panels. Spectral and joint time-frequency analyses were implemented into the instrument. This instrument can be used to display the PCG and to analyze the individual heart sound and murmur for the detection of heart valve diseases. Using a test-bed, the PCG data acquisition and analysis were performed remotely over the Internet. Through the main PCG panel, an operator can control the acquisition and analysis of PCG signals. In the remote test, real-time transmission of the PCG signal over the Internet was possible. Remote operators were able to view smoothly scrolling PCG waveforms and could control all the acquisition parameters and perform spectral and time-frequency analyses on the acquired heart sound. This study demonstrated that a LabVIEW-based medical virtual instrument provides a low-cost and flexible solution for data acquisition and analysis of PCG. It also showed that the current Internet supports the transmission of real-time PCG signals. Compared with other telemedicine systems, this application transfers not only the medical data, but also the virtual instrument and its signal processing capability through the Internet.

An analytical solution is developed to quantify a site-specific fluorophore lifetime perturbation that occurs, for example, when the local metabolic status is different from that of surrounding tissue. This solution may be used when fluorophores are distributed throughout a highly turbid media and the site of interest is embedded many mean scattering distances from the source and the detector. The perturbation in lifetime is differentiated from photon transit delays by random walk theory. This analytical solution requires a priori knowledge of the tissue-scattering and absorption properties at the excitation and emission wavelengths that may be obtained from concurrent time-resolved reflection measurements. Additionally, the solution has been compared with the exact, numerically solved solution. Thus the presented solution forms the basis for practical lifetime imaging in turbid media such as tissue.

Current functional MRI techniques are essentially based on detection of hemodynamic changes induced by neuronal activation. This study is an exploration of the feasibility of direct detection of neuronal current induced NMR phase and/or magnitude changes. Our analysis was based on the approximation that neurons exist in an infinite homogeneous conducting medium, and are represented by an infinitely long cylindrical conductor, carrying uniform current. Neuronal activation was modeled by a current dipole. Simulations were performed to evaluate the effects of the local neuronal magnetic fields on the MRI signal at 3T. The magnetic field changes and the corresponding phase changes induced from a current range of 2 nA to 100 (mu) A were estimated. The conductor diameter was varied from 10 micrometer to 1 mm corresponding to the sizes ranging from that of a single axon to that of a patch of functionally similar cortex. Current induced magnetic field effects were assessed as resolution and neuronal orientation with respect to Bo were modulated. Simulation results were compared with measurements obtained from a current phantom at 3T, for a current range of 10 - 100 (mu) A. Based on the dipole model formulated we found that neuronal currents can produce magnetic fields on the order of 1.3 pT; 0.0006 degree(s) to 9 nT; 4 degree(s) depending on the neuronal bundle diameter, orientation, and current capacity. The mechanism for signal changes is that of Bo shifts. This is fundamentally similar to that of BOLD contrast, but caused by current changes rather than susceptibility changes from changes in blood oxygenation. If the bundles are of random orientations within a voxel, the estimated fields and NMR magnitude changes (decrease in T2*) are on the order of present system detection levels of approximately 1nT; 0.5 degree(s) or 0.01% signal change. Conversely, if the bundle orientation is homogeneous, then neuronal current effects are above system detection levels. The spatial scale of the current distribution also determines the net phase shift and magnitude change. The simulation and phantom experiment results demonstrated the feasibility of using MRI to directly detect local magnetic field perturbations that can result from neuronal currents on the order of a few (mu) A. This study provides a simple model for the evaluation of the feasibility to directly measure neuronal currents with MRI. Additionally it gives a starting point for the design of appropriate imaging methods towards detecting low signal level neuronal currents. A more extensive modeling of cortical and neuronal geometry, tissue inhomogeneity, timing mechanisms, and current distributions, will provide further insight in the development of MRI experimental techniques.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Three-dimensional (3-D) color Doppler imaging of flow jets was performed to investigate the effects of flow rate and orifice size on jet volumes. Flow jets were generated using a flow model to simulate mitral regurgitation. This flow model consisted of a ventricular chamber, a valvular plate and an atrial chamber. Steady flow was driven through circular orifices having diameters of 2.5, 3.5, 4.5, and 6 mm, respectively, with flow rates of 5, 10, 15, 20, and 25 mL/s to form free jets in the atrial chamber. An ATL Ultramark 9 HDI system was used to perform 3-D color Doppler imaging of the flow jets. A transesophageal probe was rotated by a stepper motor to create 3-D color Doppler images of the jets. The color jet volumes for different hemodynamic conditions were measured and then compared with the theoretical predictions. Results showed that the jet volume estimated from the 3-D color Doppler was directly proportional to the flow rate and inversely proportional to the orifice size. The estimated jet volumes correlated well (r > 0.95) with theoretical predictions. This study supports the use of color jet volume as a parameter to quantify mitral regurgitation.

Fluorescence lifetime imaging creates a map of spatial differences in local lifetime. These lifetime differences can be correlated with local metabolic levels which makes this a valuable biomedical imaging modality. We examine the ability to localize a fluorescence signal from deeply embedded sites in highly scattering, turbid, tissue-like media. We show how a specific point-spread-like function may be used to quantify an isolated lifetime perturbation between two sites in tissue. This new minimally invasive method offers the opportunity to infer near-real-time estimates of metabolic activity.

In this paper, we present an automated multi-modality registration algorithm based on hierarchical feature extraction. The approach, which has not ben used previously, can be divided into two distinct stages: feature extraction and geometric matching. Two kinds of corresponding features - edge and surface - are extracted hierarchically from various image modalities. The registration then is performed using least-squares matching of the automatically extracted features. Both the robustness and accuracy of feature extraction and geometric marching steps are evaluated using simulated and patient images. The preliminary results show the error is on the average of one voxel. We have shown the proposed 3D registration algorithm provides a simple and fast method for automatic registering of MR-to-CT and MR-to- PET image modalities. Our results are comparable to other techniques and require no user interaction.

The photorefractive polymer based holographic memories provide the alternative of achieving terabytes of digital data storage with gigabits per seconds of transfer. The stored information in the medium is in the form of electromagnetic distribution and it is governed by the second order inhomogeneous Helmholtz equations with no known exact solution. A numerical approximation using finite element method is employed to model the wave propagation in the polymer. The model is used to approximate the Helmholtz equation for a spatially varying index of refraction medium subject to input of polarized planar waves. The initial results from the mathematical formulation conform to the theoretical expectation of wave propagation on the non-conducting and non-magnetic materials. In addition, assuming periodicity of the inhomogeneous material, the reflective waves are computed and conservation of energy on the medium is verified. The model is used to simulate electromagnetic wave propagation through a two- dimensionally varying index of refraction poly (N- vynilcarbazol) (PVK) polymer. The transmitive waves are computed at various angles of incidents. The stored hologram represented as transmitive wave is reconstructed in reading process.

Intrinsic and exogenous fluorescent molecules may be used as specific markers of disease processes, or metabolic status. A variety of fluorescent markers have been successfully used for transparent tissue, in-vitro studies, and in cases where the markers are located close to the tissue surface. For example, given fluorescence lifetime measurements of a fluorophore such as bis(carboxylic acid) dye, the known relationship of pH on its lifetime may be used to determine the pH of tissue at the fluorophore's location. For fluorophore depths greater than approximately one millimeter in normal tissue, such as might be encountered in in vivo studies, multiple scattering makes it impossible to make direct measurements of characteristics such as fluorophore lifetime. In a multiple scattering environment, the collected intensity depends heavily on the scattering and absorption coefficients of the tissue at both the excitation and emission frequencies. Thus, to obtain values for specific fluorophore characteristics such as the lifetime, a theoretical description of the complex photon paths is required. We have applied Random-walk theory to successfully model photon migration in turbid medias such as tissue. We show how time-resolve intensity measurements may be used to determine fluorophore location and lifetime even when the fluorophore site is located many mean photon scattering lengths from the emitter and detector.

We have developed and tested with numerical simulations a closed-form solution based on random walk theory for quantifying the lifetime of typical near infrared fluorophores embedded 5mm or greater in turbid media.

Medical images contain information (in two, three, and four dimensions) that is important to the clinician in diagnosis, evaluation, and treatment. New methodologies and tools make it possible to extract, interpret, integrate, and display that information very effectively. This paper describes some of the application areas, gives examples of the tools now available and under development, identifies the benefits and costs of the new approaches, examines the issues in evaluation and validation, and looks at opportunities for further improvements.

Fluorescence lifetime imaging is a useful tool for quantifying site-dependent environmental conditions in tissue. Fluorophores exist with known lifetime dependencies on factors such as concentrations of O2 and other specific molecules, as well as on temperature and pH. Extracting fluorophore lifetime for deeply embedded sites in turbid media such as tissue is made difficult by the multiple scattering of photons traveling through tissue. This scattering introduces photon arrival delays that have similar characteristics to the delays resulting from the excitation and subsequent emission of photons by fluorophores. Random walk theory (RWT) provides a framework in which the two sources of diffusion-like delays can be separated so that the part due to fluorescent lifetime can be quantified. We derive a closed-form solution that predicts time-resolved photon arrivals from a deeply embedded fluorophore site. The solution requires that an average absorption coefficient be used. However, it is shown that this assumption introduces only a small error. This RWT-derived solution is also shown to be valid for a range of geometries in which the fluorophore site is embedded at least 10 mean scattering lengths and in which the fluorophore lifetime is less than 1 ns.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The detection of sources responsible for coherent components in ultrasound signals, has been a difficult task. In this work, we explore the idea of using phase coherence as a measure of the level of structured regularity present in the scattering medium. If the scattering sites are located randomly, then the reflected signal should be incoherent. This is the case of purely diffuse scattering. If, however, there is structure in the scattering medium, then the reflections from those sites will have some non-random phase relationship. In this work, the phase distribution is characterized as follows. For each demodulation frequency, we plot the power as a function of phase. This is computed for all frequencies in the usable bandwidth of the transducer. For each frequency, the power is uniformly distributed across phase from 0 to 2 (pi) for a purely incoherent signal. Systematic deviations from the uniform distribution may indicate the presence of coherent scattering components. This approach was first verified using simulation data, then applied to two sets of clinical ultrasound data. We have achieved good classification performance (area under the ROC curve, Az equals 0.86 +/- 0.04) using two features extracted from this analysis of phase.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.