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Image registration is a correlation procedure that allows either the complementary study of images obtained from different modalities, or enables the analysis of images obtained by the same modality but at different times. Applied to a variety of clinical and investigational problems, image registration can offer a major advance in diagnostic imaging. In this paper, we present an automated multi-modality registration algorithm based on hierarchical feature extraction. Two kinds of shape representations -- edges and surfaces (skin surface, inner skull surface, and outer brain surface) -- are extracted hierarchically from different image modalities. The registration is then performed using the user- specified (but automatically extracted) corresponding features. Both the robustness of the algorithm and the registration accuracy using different registration features are compared in this paper. The preliminary results show that the use of edge and surface features could succeed over a large range of geometric displacements. The results also indicate that neither the edge nor the surface feature is clearly superior in terms of registration accuracy. Using the edge feature could, however, have the advantage of eliminating the surface segmentation step which requires extra complexity, variability, and time cost. We have shown the proposed 3D registration algorithm provides a simple and fast method for automatic registration of CT and MR image modalities. Preliminary results using our registration algorithm are comparable to those obtained by other techniques.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Failure to align images accurately often is due to the optimization algorithms being trapped in local maxima or spurious global maxima of the mutual information function. Strategies contemplated to improve registration involve modifying the optimization scheme or the registration measure itself. We recently found that normalized mutual information (for 2D image registration) provides a larger capture range and that is more robust, with respect to the optimization parameters, than the non-normalized measure. In this paper we assessed the utility of a stochastic global optimization technique for image registration using normalized and non-normalized mutual information. By conducting large-scale studies with patient data in 2D, we established a success rate baseline with the local optimizer only. Formal proof has not yet been found that incorporating the global optimizer does not impair performance. However, experiments to date indicate that its inclusion leads to better (i.e., higher probability of correct convergence) overall performance. More over, studies now underway show good effectiveness of our approach in a variety of 3D cases.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We present preliminary results of a study in which Fractal Interpolation Function Models (FIFM) are used to generate a fractal dimension (fd) feature to discriminate between benign and malignant masses on digitized mammograms. The FIFM method identifies boundary segments that are approximately self-affine and can be accurately modeled with multiple fractal interpolation functions (FIF). The fd of a segment is estimated to be the mean of the fds from the FIF models of that segment. An overall fd feature is computed as the mean of multiple segment fds. The statistical approach provides a stability to the overall fd feature. The FIFM feature may be useful in improving the performance of computer-assisted-diagnosis systems.

Blood vessels overlying one another at distinct depths (and hence appearing to intersect) in the sclera of the eye can be distinguished reliably from those that in fact do branch within the same depth, using only the information contained in a single photograph of the conjunctiva. That conclusion arises from extension of earlier work that qualitatively inferred relative depth of vessels. The current research was motivated by the need to quantify such inferences in terms of their sensitivities and robustness. A physics first principles model forms the basis for selection of features that capture blood vessel depth information. Features extracted from the image are shown to be useful in that effort; their utility is verified with phantoms that mimic the behavior of the conjunctiva and sclera. Because no special preparations are needed, the method works as well on archived images as on newly-acquired ones, and thus can be used in retrospective studies of images of the eye and other diffuse media.

MRI has been proposed as an alternative method to mammography for detecting and staging breast cancer. Recent studies have shown that architectural features of breast masses may be useful in improving specificity. Since fractal dimension (fd) has been correlated with roughness, and border roughness is an indicator of malignancy, the fd of the mass border is a promising architectural feature for achieving improved specificity. Previous methods of estimating the fd of the mass border have been unreliable because of limited data or overlay restrictive assumptions of the fractal model. We present preliminary results of a statistical approach in which a sample space of fd estimates is generated from a family of self-affine fractal models. The fd of the mass border is then estimated from the statistics of the sample space.

Various researchers have used texture analysis to perform ultrasound tissue characterization with mixed results. Others have used spectral parameters to classify tissue. Several groups have used a feature attributed to the mean scatterer spacing (MSS) as a discriminating feature for tissue classification. We have previously shown that the locations of peaks in the complex cepstrum (PCEP) provide a good estimate of MSS in phantom and simulation studies. In this work, we show that by combining PCEP with texture-based features, we are able to distinguish between normal and hepatitis patients with a high degree of accuracy.

Previous image registration schemes based on mutual information use Shannon's entropy measure, and they have been successfully applied for mono- and multimodality registration. There are cases, however, where maximization of mutual information does not lead to the correct spatial alignment of a pair of images. Some failures are due to the presence of local or spurious global maxima. In this paper we explore whether the normalization of mutual information via the use of a weight based on the size of region of overlap, improves the rate of successful alignments by reducing the presence of suboptimal extrema. In addition, we examine the utility of a deterministic entropy measure. The results of the present study indicate that: (1) the normalized mutual information provides a larger capture range and is more robust, with respect to optimization parameters, than the non-normalized mutual information, and (2) the optimization of mutual information with the deterministic entropy measure takes, on average, fewer iterations than when using Shannon's entropy measure. We conclude that the normalized mutual information using the deterministic entropy measure is a faster and more robust function for registration than the traditional mutual information.

This paper presents a new method of estimating the distance between reguIarIyspaced coherent scatterers within . soft tissue from backscattered radio-frequency (RF) signals. This scatterer spacing has been used successfully to classify tissue type, diagnose diffuse liver and kidney disease, and to diagnose Hodgldn's disease involvement in the spleen. This new method makes use of the complex cepstrum to identify periodic structure in the backscattered ultrasound signals. Periodic components in the time domain RF signal manifest themselves as peaks in the quefrency (cepstral) domain. The task of estimating the scatterer spacing is then reduced to identifying peaks in the cepstruni. Using simulation data, we show that peaks in the quefrency domain (corresponding to known periodic components in the RB signal) are easier to detect when the cepstrum is computed using the complex cepstrum rather than the commonly used power cepstrum. Similar improvements are seen using phantom and in vivo liver data. Keywords: ultrasound, scatterer spacing, power cepstrum, complex cepstruni, periodicity.

This paper presents a new method of estimating the distance between regularly-spaced coherent scatterers within soft tissue from backscattered radio-frequency (RF) signals. Periodic components in the RF signal manifest themselves as peaks in the quefrency (cepstral) domain. Using simulation data, we show that these peaks are easier to detect using the complex cepstrum rather than the commonly used power cepstrum. Similar improvements are seen using phantom and in vivo liver data.

To achieve higher compression ratios in medical images while preserving quality, a new fractal method is developed, examined, and tested in this paper. The basic idea of fractal image compression is to reduce the similarity redundancy by identifying a given image with the fixed-point of an appropriate partitioned iterated function system (PIFS), that consists of a set of contractive affine transformations. Since conventional PIFS models use only affine transformations to represent similarity for the whole image, the quality of the re-created images is quite limited in some cases. Because the grayscale in most images is dependent on location, it is not sufficient to describe the relationship using linear transforms when there are complex textures present. Effective interpolation polynomials should adapt to the nature of the underlying texture; that is the basis of the new method. The new fractal image-compression algorithm uses adaptive PIFS (APIFS), that is based on variants of affine transformations and lossless compression methods. Polynomials of various orders are used to represent adaptively the similarity of grayscale based on the local details of the image, and the contractive condition for the generalized transformation is shown to hold. In this approach, quadratic and linear models are applied adaptively to the contractive transformations. The variants of the affine transform are used where similarities can be identified, while lossless compression techniques are used for those local areas in which similar domains do not exist or cannot be found. Preliminary experiments indicate that the APIFS model has the potential to increase the useful compression ratio. Experiments with medical images indicate that this new algorithm can be extended to yield a compression ratio of about 30:1 without perceptible degradation.

Classifier-independent feature analysis is a classic problem whose solution exhibits exponential growth. In previous research, we developed a new approach to classifier-independent feature analysis based on relative feature importance (rfi), a metric for the relative usefulness of the feature in the optimal subset. Because finding the optimal subset requires exhaustive search, we have also developed an estimator for rfi. The estimator uses adaptive techniques to reduce the computational load. The implementation of both algorithms, direct calculation of rfi and the estimator, on a Connection Machine (CM-5) in CM Fortran is described in this paper. Direct calculation of rfi lends itself naturally to implementation in CM Fortran because the computationally intensive components of the algorithm involve manipulation of large arrays. The adaptive nature of the estimator, however, makes implementing it on the CM-5 more challenging and less efficient.

We examine the entire imaging chain, from acquisition to interpretation and storage, and indicate the opportunities for applying state-of-the-art techniques throughout that chain. Implications for quality and cost of care, including the impact on what is considered to be the standard of care, are discussed. Attention is drawn to the complementary roles played by large databases on one hand, and powerful comparative and quantitative methods on the other. The relevance of well-designed user interfaces, and of psychovisual considerations, is indicated, and their interaction with various compression algorithms is discussed. The benefit to diagnosis of knowledge-based approaches is considered.

The conjunctiva is an ideal location to study and measure the morphology of the microcirculation, because access to blood vessels at this site is essentially non-invasive. Our efforts have been directed toward automating the labor-intensive process of collectingmorphological information from photographs and video images of the conjunctiva. Inprevious work we have developed a detailed model of the illumination/reflection processesthat result in a film or video image of the bulbar conjunctiva. Using information gained fromthis model, we have now extended our research toward the development of robustmicrovessel detection and tracking algorithms. Our modeling has shown that it is possibleto extract some relative three-dimensional information about blood vessels from their gray-scale profiles. Images of the conjunctiva, however, also exhibit significant variability in theirgray-scale data. We have adapted some fuzzy logic concepts to deal with this problem.These fuzzy logic algorithms have been very effective in detecting blood vessel points in

Demonstrably effective enhancement of portal films is now available to practicing radiation therapists. The combination of a straightforward user interface and an algorithm that runs in a clinically-reasonable time improves our previously-reported technique and makes it accessible to clinicians. Radiation portal images remain the most important mechanism for assuring geometric accuracy of radiation therapy delivery. The high-energy x-ray beams, however, produce films that are intrinsically of low contrast and so they can vary widely in quality, making consistent interpretation difficult. Our current work improves image quality further by adding the option for a median-filtering operation at the output of SHAHE. This step removes much of the local noise that is sometimes introduced despite the contrast- limiting step. Using a new graphical-user-interface-builder, we have built a portal image enhancement system designed to be used by the non-expert. Four levels of enhancement are available: high and low contrast, with and without the median filter. Adjustment of all seven SHAHE parameters is possible for the expert user, but is discouraged for routine use because many of the regions of 'enhancement space': have not been explored for accuracy. The user of the system is presented with a display that allows the selection of an input image and the level of enhancement. Typical computation times (for a 2048 x 2048 image) on a Sun Sparc 400 computer average approximately 10 minutes. Clinical portal imaging -- especially as digital capture is introduced -- should be able to benefit measurably from the use of the methods described here.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The accuracy of computer-aided detection (CAD) schemes involves a tradeoff between high sensitivity and low false-positive rate. In an on-going study, we are analyzing our CAD scheme for the detection of clustered microcalcifications in digital mammograms to determine the causes of false-negative and false-positive clusters. Two different limitations that lead to false-negatives and false-positives have been identified. The first limitation is imposed by the quality of the digital mammogram, whereas the second is a consequence of the similarities of radiographic features between true and false clusters. In this paper, we examine the effects of image quality, particularly image noise, on the performance of our CAD scheme. Preliminary results indicate that the performance of our scheme is limited by anatomic noise and x-ray quantum noise. Almost all the false positives detected in clinical images by our CAD scheme are caused by a combination of these two forms of noise.

Feature selection for classification is the process of determining the discriminatory power of features. Discriminatory power is the relative usefulness of a feature for classification. Traditional feature selection techniques have defined discriminatory power in terms of a particular type of classifier. We present an alternative approach that uses a new metric for discriminatory power called relative feature importance (RFI). RFI ranks features by discriminatory power based on their distributional structure, without parametric assumptions. Because computing RFI is not, in general, practicable, we also present a technique that efficiently estimates RFI. This hybrid technique, the genetic neural RFI estimator (GENFIE), has accurately estimated RFI in preliminary studies.

We have designed and implemented a distributed system that allows access to text and images that reside in the Teleradiology Archive, from any node in our local area network. The textual data is organized into an SQL database with an X Window based graphical user interface and images are stored in an optical disk. The archive is currently in use in various research projects within the department. Also, in collaboration with industry, it has been incorporated into a multimedia research and development system.

This research is in support of the development of an image processing system which is capable of detecting and tracking blood vessels in photographs or video images of the human microcirculation system. We describe a model which replicates the illumination processes contributing to a film or video image of the microvessels of the human bulbar conjunctiva. The model provides a foundation for microvessel detection algorithms, for measurement of vessel parameters, for determining relative depth of blood vessels, and for separating neighboring vessels in complex images. The model is based on a cylindrical vessel embedded in a diffuse medium which is on a reflecting background. A light source illuminating the scene is reflected by it's components and passes through a pinhole to an image plane, which records these reflections as intensity values at discrete pixel locations. Fundamental physical principles which include Lambert's cosine law, isotropic spreading, Fresnel's law and Beer's law are systematically applied to the model. A video apparatus and a phantom were constructed to analyze different illumination conditions and to verify the model. A simulation based on the model compared favorably with data taken from phantom images.

Earlier studies of the human vision system suggest that a complex object may be recognized from the qualitative features associated with its boundary. Since the human vision system uses qualitative features for object recognition, it is extremely robust to deformation caused by noise, obstruction, scale change, or optical distortion. We considered various qualitative descriptors for digital images. The descriptors developed in this research are based on the estimated curvature function from digital boundaries and can discriminate features such as straightness of a contour segment, perpendicularity of two contour segments, parallelness of two straight contour segments, parallelness of two curved contour segments in a global sense, and the direction of convergence if two straight contour segments are not parallel. We demonstrated that the qualitative descriptors developed in this paper can be applied for identification of elementary shapes, such as cylinders, bricks, cones, etc. We also discussed the recognition of more complex shapes after decomposing them into simpler components.

A diffusion-like process, analogous to the thermodynamic diffusion of heat or of gas molecules, is used to describe the shape of two-or three-dimensional objects. It is effective at identifying extrema of curvature that might be used to segment the boundary, and also at characterizing the types of line segments that lie between the extrema. Both of those operations are essential for qualitative descriptions of images, as would be required by an approach based on geometrical icons (geons). The region need not be convex. The descriptor is invariant to several common transformations, including rotation. It can be implemented easily on parallel machines, does not pose problems with the definition of slope, and appears to be capable of dealing with the matching of partially-occluded objects. The descriptor’s performance is essentially independent of user-supplied parameters.

A combined-transform coding (CTC) scheme is proposed to reduce the blocking artifact of conventional block transform coding and hence to improve the subjective performance. The proposed CTC scheme is described and its information-theoretic properties are investigated. Computer simulation results for a class of chest X-ray images are presented. A comparison between the CTC scheme and the conventional discrete cosine transform (DCT) and discrete Walsh-Hadamard transform (DWHT) demonstrates the performance improvement of the proposed scheme. In addition, combined coding can also be used in noiseless coding, yielding a slight improvement in the compression performance if it is used properly. >

The design, implementation, and testing of a computational observer method for objective evaluation of ultrasound images are presented. The method uses digitized ultrasound B-scan images of a test phantom (the contrast-detail phantom), and is able to calculate the detectability of a target (signal) from its background (background noise). A quantitative detectability index, based on the measured signal-to-noise ratio of the image data, that is measured for both the human and the computational observer on the same scale is generated. It is shown that the computational observer (CO) method may be a more useful, objective way of evaluating ulrasound images and imaging systems than methods that rely solely on human observers. It may also be applicable to other types (i.e. other than ultrasound) of imaging systems which produce noisy images. The relevance of the CO method when compared to human observer two-alternative-forced-choice (2AFC) readings of the same data by showing a high correlation between the CO detectability results and those of human observers for the same set of images. The method is (1) quantitative, (2) reproducible, (3) absolute, (4) takes into account, and can calculate the value of TPF and FPF for each target, for the given system, and (5) speeds up the evaluation of an image or imaging system (compared to using human observers), given the right conditions and equipment. >

Transform coding has been used successfully for radiological image compression in the picture archival and communication system (PACS) and other applications. However, it suffers from the artifact known as 'blocking effect' due to division of subblocks, which is very undesirable in the clinical environment. In this paper, we propose a combined-transform coding (CTC) scheme to reduce this effect and achieve better subjective performance. In the combined- transform coding scheme, we first divide the image into two sets that have different correlation properties, namely the upper image set (UIS) and lower image set (LIS). The UIS contains the most significant information and more correlation, and the LIS contains the less significant information. The UIS is compressed noiselessly without dividing into blocks and the LIS is coded by conventional block transform coding. Since the correlation in UIS is largely reduced (without distortion), the inter-block correlation, and hence the 'blocking effect,' is significantly reduced. This paper first describes the proposed CTC scheme and investigates its information-theoretic properties. Then, computer simulation results for a class of AP view chest x-ray images are presented. The comparison between the CTC scheme and conventional Discrete Cosine Transform (DCT) and Discrete Walsh-Hadmad Transform (DWHT) is made to demonstrate the performance improvement of the proposed scheme. The advantages of the proposed CTC scheme also include (1) no ringing effect due to no error propagation across the boundary, (2) no additional computation and (3) the ability to hold distortion below a certain threshold. In addition, we found that the idea of combined-coding can also be used in noiseless coding, and slight improvement in the compression performance can also be achieved if used properly. Finally, we point out that this scheme has its advantages in medical image transmission over a noisy channel or the packet-switched network in case of channel error and packet loss.

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. The rate-distortion properties are also discussed in terms of different 1-D and 2-D correlation models.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Compression methods based on Gabor functions are implemented for simulated nuclear medicine liver images with and without simulated lesions. The quality of the compression schemes are assessed objectively by comparing the original images to the compressed images through calculation of the Hotelling trace, an index shown to track with human observers for images from this modality.1 For compression based on thresholding the complex Gabor coefficients, better than 2: 1 compression is obtained without significant degradation in image quality.

We report a method for evaluating the performance of model observers for decompressed images in analytical form using compression noise statistics. It derives test statistics and detectabilities for the ideal observer, the nonprewhitening observer, the Hotelling observer, and the channelized Hotelling observer (CHO) on decompressed images. The derived CHO performance is validated using the Joint Photographic Experts Group (JPEG) compression algorithm. The validation results show that the derived CHO receiver operating characteristics (ROCs) and areas under ROC curves predict accurately their corresponding estimated values. These analytical-form quality measures of decompressed images provide a way to optimize compression algorithms analytically, subject to a model-observer performance criterion. They also provide a theoretical foundation for efforts to create a model observer for decompressed images.

This work assesses the usefulness of an objective, task-based image quality measure that is correlated with perceived image quality; the measure uses the most salient features contained within a medical image. Contributions include the development of a perceptually correlated metric that is useful for quantifying the salience of local, low-level visual cues and identifying those spatial frequencies that are most distinct and perhaps most relied upon by radiologists for decision making. A set of 40 mammograms and registered eye position data from nine observers was used to evaluate the salience metric. A parsimonious analysis-of-variance model explained the variance in the salience results. This analysis is generalized to a population of readers and cases. An analysis of salience versus time of first eye fixation shows good correlation with true positive lesions that were found by experienced readers in less than 2 s.

Increasing evidence suggests that inflammation may contribute to the process of carcinogenesis. This is the basis of several clinical trials evaluating potential chemopreventive drugs. These trials require quantitative assessments of inflammation, which, for the oral epithelium, are traditionally provided by histopathological evaluation. To reduce patient discomfort and morbidity of tissue biopsy procedures, we develop a noninvasive alternative using diffuse reflectance spectroscopy to measure epithelial thickness as an index of tissue inflammation. Although any optical system has the potential for probing near-surface structures, traditional methods of accounting for scattering of photons are generally invalid for typical epithelial thicknesses. We develop a single-scattering theory that is valid for typical epithelial thicknesses. The theory accurately predicts a distinctive feature that can be used to quantify epithelial thickness given intensity measurements with sources at two different angles relative to the tissue surface. This differential measure approach has acute sensitivity to small, layer-related changes in scattering coefficients. To assess the capability of our method to quantify epithelial thickness, detailed Monte Carlo simulations and measurements on phantom models of a two-layered structure are performed. The results show that the intensity ratio maximum feature can be used to quantify epithelial thickness with an error less than 30% despite fourfold changes in scattering coefficients and 10-fold changes in absorption coefficients. An initial study using a simple two-source, four-detector probe on patients shows that the technique has promise. We believe that this new method will perform well on patients with diverse tissue optical characteristics and therefore be of practical clinical value for quantifying epithelial thickness in vivo.

This PDF file contains the editorial “Guest Editorial: Special Section on Biomedical Image Representation” for JBO Vol. 8 Issue 01

A method is presented that uses a diffusion-like process to describe the shape of a region. Convexity is not required, the descriptor is invariant under several common transformations, is applicable in the n-dimensional case, and is easy to compute.

The application of a procedure for classifying tissue types from unlabeled acoustic measurements using unsupervised analysis is reviewed and evaluated. Unsupervised learning techniques are applied to the problems of detecting tumors within an organ and of discriminating between tissue types of two neighboring organs such as the liver and the kidney. >

A method is presented for determining whether two given regions are adjacent, and for finding all the neighbors of different sizes for a given region. Regions are defined as elementary squares of any size. In a companion paper [ 2 ], we introduce the quadcode and discuss its use in representing geometric concepts in the coded image, such as location, distance, and adjacency. In this paper we give a further discussion of adjacency in terms of quadcodes. Gargantini [ 1 ] discussed adjacency detection using linear quadtrees. Her discussion was applied to pixels, and a procedure was given to find a pixel's southern neighbor only. This paper considers elementary squares of any size, and gives procedures for both aspects of the problem: for determining whether two given regions are adjacent, and for finding all the neighbors of different sizes for a given region.

We divide the boundary of a 2-dimensional object into segments each of which is either straight or a circular arc; associated with the segment end-points are angle measures that can be used to match an object with a transformed (rotated, scaled) version of itself. The chain code, easily extracted from the boundary pixels, is the basis of this division. The approach avoids problems common to many of the existing methods for identification of curvature extrema: sensitivity to noise and dependence on parameters that are chosen empirically. To each section of the boundary we assign a code that represents the change in slope between it and the previous section. This set of codes is integrated and thus provides a measure of the total directional change relative to the first section. For a closed object, the sequence of these sums is periodic, and one cycle can be plotted as a function of arc length, s. Such a plot can be shown to contain only straight lines: those that are not parallel to the s axis (representing circular arcs on the original boundary of the object), and those that are (representing straight sections on the boundary). This paper describes a recursive procedure for dividing the digital version of the curve described above into its linear segments. Each segment represents an arc that is the best fit to a portion of the original boundary; the angle which is defined by the arc is identical to the angle change of the edge in the same section, and the length of the arc is identical to that of the edge. The recursive procedure measures the error (for each value of arc length) between a proposed fitting line and the actual value of cumulative angle; where the error is maximum, and above a threshold, the line is segmented. The procedure is repeated until the error is sufficiently small. The breakpoints thus indicate the location and value of points of greatest curvature change. A formal definition of the procedure is given, and it is shown to perform well for rotated, scaled, and noisy objects.

A new objective method for hearing threshold testing, parametric modeling, is proposed in this paper. The fifth-order autoregressive models are estimated for the averaged BAEP records at a series of stimulus levels. The normalized prediction error enor is chosen as the hearing threshold measure. For normal hearing subjects, the values of enor are very different between the control condition and the click stimulus. For abnormal patients with different hearing loss problems, the estimated hearing thresholds in terms of enor show a good agreement with the physicians' assessment.

A new coding scheme, namely combined coding (CC) is proposed in this paper. The main point behind CC is to divide the image into two image sets with different statistical properties, then exploit those properties to code the sets more efficiently. Three variations are distinguished and discussed. Two particular CC schemes are identified as CC scheme 1 and CC scheme 2. For the CC 1, i.e. Run-length mapping then entropy coding for the upper image set (UIS) and Peano-differential mapping then Ziv-Lempel coding for the lower image set (LIS), the experimental results are tabulated and compared with other methods. For CC scheme 2, i.e. same coding method for the UIS and transform coding the LIS, both the rate-distortion performance and actual coding results are given. It is noted that the CC significantly reduces the "blocking effect" and this property is very useful for clinical decisions. This scheme seems to provide a good tradeoff and an efficient solution to practical image transmission and archival applications.

This paper describes a procedure for classifying tissue types from unlabeled acoustic measurements (data type unknown) using unsupervised cluster analysis. These techniques are being applied to unsupervised ultrasonic image segmentation and tissue characteriza-tion. The performance of a new clustering technique is measured and compared with supervised methods, such as a linear Bayes classifier. In these comparisons two objectives are sought: a) How well does the clustering method group the data? b) Do the clusters correspond to known tissue classes? The first question is investigated by a measure of cluster similarity and dispersion. The second question involves a comparison with a supervised technique using labeled data.

In this paper, we propose a new method to construct the joint probability model from a specified first-order distribution and correlation structure. The construction procedure can be interpreted in two ways: (1) It embodies the maximum entropy principle, or (2) It is considered as a correlated non-Gaussian source generated by a nonlinear transformation from a correlated Gaussian source. Its stochastic properties [mean,correlation] and information-theoretic properties [entropy, rate-distortion bound] are examined. An example for the lognormal distribution is given to illustrate the construction process and the characteristics of the source. This approach should remove the limitations imposed by earlier methods, and make for more realistic modeling of medical images.

A method is presented that uses a diffusion-like process to describe the shape of a region. Convexity is not required, the descriptor is invariant under several common transformations, is applicable in the n-dimensional case, and is easy to compute.