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This paper describes a program for summarizing scatter plots with robust ellipses. The program is written in BASIC and is designed for interactive use on a microcomputer graphics terminal to enable quick redesigns of the graph.
Generating random variables from a specific distribution, whether symmetric or asymmetric, is a concern of investigators involved in Monte Carlo studies. Of particular interest to those concerned with robustness is the generation of contaminated symmetric distributions such as those used in the Princeton Robustness Study. A reliable composite uniform U(0,1) generator is described and algorithms for transforming U(0,1) to symmetric long-tailed and contaminated symmetric distributions are given. Goodness-of-fit tests and graphical illustrations demonstrate the adequacy of the empirical distributions.
An updated version of the computer program EXCAD [1] allows the user to optimize experimental design to estimate parameters of particular semi-parametric models. The semi-parametric models take the general form of a function of time (t): Y(t) = NL (c(t,alpha),beta). The function NL(C(t,alpha),beta), is, in general, a non-linear transformation of a function, C(t,alpha), that in turn is the convolution of two others. One of these two functions is expressed in a non-parametric form, and is not of direct interest to the experimenter. The other is of direct interest: it is a parametric function depending on a set of parameters alpha. This semi-parametric model applies to numerous kinds of biological experiments, such as pharmacokinetic/pharmacodynamic, physiological, circulatory flow experiments. This paper presents a new method for determining an optimal experimental design to estimate the parameters alpha and beta. The new approach adopts the D-optimal criterion, and is illustrated using real thiopental data.
The need for a knowledge based expert system for efficient decision making in the field of pathology has been well accepted. To build the knowledge-base for such an expert system is a painstaking task. This work is an attempt to provide the pathologists a powerful and user-friendly tool that will help them in the process of building the knowledge-base for medical diagnosis by closely looking at the specimen images and their extracted feature values. The tool is based on multi-media relational database and software like VAX Rally (4GL), ILIAD and SAS to provide a wide-range comparative study between feature data and image data; and also their statistical analysis.
A recently described method for speed of sound estimation in tissues in pulse-echo mode involves reception of echoes generated by an ultrasonic pulse by means of a linearly tracking transducer. When the peaks of echo amplitudes are used as markers of arrival time, stairstep-like artifacts appear in the echo arrival time vs. transducer position plots. We postulate that these artifacts are a consequence of the speckle phenomenon commonly encountered in ultrasonic imaging. To test this hypothesis, we report computer simulations and water tank experiments which demonstrate similarities between the behavior of the stairsteps and the properties of ultrasonic speckle. Additionally, equations describing the precision of the speed of sound estimation in terms of the second order statistical properties of the stairstep artifact are derived.
Helical macromolecular assemblies are particularly difficult to study by X-ray diffraction but are quite well suited to analysis by electron microscopy. Most of our information about helical macromolecular assemblies has come from the electron microscope but has been limited to about 25 A resolution. With the use of low-dose electron cryomicroscopy, one can obtain structural data to near atomic resolution on two-dimensional crystals, but the problem is to extract the information from the noise. In this paper we present methods to extract signal from low-dose electron cryomicrographs of helically symmetric structures. We apply these methods to extract 10 A data from the bacterial flagellar filament.
A methodology able to derive spatial geometric models from input sequences of sonographic slices is proposed. The developed modeling procedure can be utilized to perform computer-assisted anatomic 3D analysis both on all echo space and selected subregions. The modeling procedure is mainly composed of three sequential phases: a) automatic acquisition and preprocessing of time sequences of 2D echotomograms; b) 3D reconstruction of images and computing of discrete distance maps of selected echoes according to predefined projective laws; and c) generation of a spatial geometric model of the examined object starting from the previously computed maps.
Large scale genomic studies are generating significant amounts of data on the structure of cellular networks. This is in contrast to kinetic data, which is frequently absent, unreliable or fragmentary. There is, therefore, a desire by many in the community to investigate the potential rewards of analyzing the more readily available topological data. This brief review is concerned with a particular property of biological networks, namely structural conservations (e.g. moiety conserved cycles). There has been much discussion in the literature on these cycles but a review on the computational issues related to conserved cycles has been missing. This review is concerned with the detection and characterization of conservation relations in arbitrary networks and related issues, which impinge on simulation simulation software writers. This review will not address flux balance constraints or small-world type analyses in any significant detail.
Conventional methods to estimate the number of dentinal tubules cannot be considered reliable and repeatable, because results depends on the operator outlining of the tubules contours. In this study, we propose a totally automated computerized analysis technique to evaluate dentinal tubules and their surface area. The comparison test of these conventional with a semi-automatic methods shows that the automated analysis allowed a reliable identification and numbering of dentinal tubules, by means of high-quality images. No statistically significant difference exists in the number of tubules and the total tubule surface area between the control and test groups.
The research presented in this article is aimed at the development of an automated imaging system for classification of normal tissues in medical images obtained from computed tomography (CT) scans. This article focuses on comparing the discriminating power of several multi-resolution texture analysis techniques using wavelet, ridgelet, and curvelet-based texture descriptors. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The algorithms are extensively tested and results are compared with standard texture classification algorithms. Tests indicate that using curvelet-based texture features significantly improves the classification of normal tissues in CT scans.
We describe an automatic and reproducible method to analyze the histological design of the cerebral cortex as applied to brain sections stained to reveal myelinated fibers. The technique provides an evaluation of the distribution of myelination across the width of the cortical mantle in accordance with a model of its curvature and its intrinsic geometry. The profile lines along which the density of staining is measured are generated from the solution of a partial differential equation (PDE) that models the intermediate layers of the cortex. Cortical profiles are classified according to significant components that emerge from wavelet analysis. Intensity profiles belonging to each distinct class are normalized and averaged to produce area-specific templates of cortical myelo-architecture.
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The application of statistical classification methods to in vivo functional neuroimaging data makes it possible to explore spatial patterns in task-related changes in neural processing. Cluster analysis is one group of descriptive statistical procedures that can assist in identifying classes of brain regions that exhibit similar task-related functionality. In practice, a limitation of cluster analysis is that the performances of clustering algorithms rely on unknown characteristics of the data, making it difficult to determine which procedure best suits a particular analysis. We present a multiple classification approach that incorporates numerous algorithms, evaluates the associated classifications, and either selects a plausible partition relative to the others considered or pools the results from the numerous methods. The multiple classification approach utilizes a new performance criterion, called the relative information (RI) measure, to evaluate the quality of the candidate partitions and as the basis for producing a composite classification image. Employing multiple classifications, rather than a single algorithm, our methodology increases the chance of detecting the functional relationships within the data and, therefore, produces more reliable results. We apply our methodology to a PET study to explore spatial relationships in measured brain function associated with increasing blood alcohol concentration levels, and we perform a simulation study to evaluate the performance of RI.
A variety of voxel-based morphometric analysis methods have been adopted by the neuroimaging community in the recent years. In this commentary we describe why voxel-based statistics, which are commonly used to construct statistical parametric maps, are very limited in characterizing morphological differences between groups, and why the effectiveness of voxel-based statistics is significantly biased toward group differences that are highly localized in space and of linear nature, whereas it is significantly reduced in cases with group differences of similar or even higher magnitude, when these differences are spatially complex and subtle. The complex and often subtle and nonlinear ways in which various factors, such as age, sex, genotype and disease, can affect brain morphology, suggest that alternative, unbiased methods based on statistical learning theory might be able to better quantify brain changes that are due to a variety of factors, especially when relationships between brain networks, rather than individual structures, and disease are examined.
Manual region tracing method for segmentation of infarction lesions in images from diffusion tensor magnetic resonance imaging (DT-MRI) is usually used in clinical works, but it is time consuming. A new unsupervised method has been developed, which is a multistage procedure, involving image preprocessing, calculation of tensor field and measurement of diffusion anisotropy, segmentation of infarction volume based on adaptive multiscale statistical classification (MSSC), and partial volume voxel reclassification (PVVR). The method accounts for random noise, intensity overlapping, partial volume effect (PVE), and intensity shading artifacts, which always appear in DT-MR images. The proposed method was applied to 20 patients with clinically diagnosed brain infarction by DT-MRI scans. The accuracy and reproducibility in terms of identifying the infarction lesion have been confirmed by clinical experts. This automatic segmentation method is promising not only in detecting the location and the size of infarction lesion in stroke patient but also in quantitatively analyzing diffusion anisotropy of lesion to guide clinical diagnoses and therapy.
Diffusion tensor imaging (DTI) has seen increased usage in clinical and basic science research in the past decade. By assessing the water diffusion anisotropy within biological tissues, e.g. brain, researchers can infer different fiber structures important for neural pathways. A typical DTI data set contains at least one base image and six diffusion-weighted images along non-collinear encoding directions. The resultant images can then be combined to derive the three principal axes of the diffusion tensor and their respective cross terms, which can in turn be used to compute fractional anisotropy (FA) maps, apparent diffusion coefficient (ADC) maps, and to construct axonal fibers. The above operations all assume that DTI images along different diffusion-weighting directions for the same brain register to each other without spatial distortions. This assumption is generally false, as the large diffusion-weighting gradients would usually induce eddy currents to generate diffusion-weighting direction-dependent field gradients, leading to mis-registration within the DTI data set. Traditional methods for correcting magnetic field-induced distortions do not usually take into account these direction-dependent eddy currents unique for DTI, and they are usually time-consuming because multiple phase images need to be acquired. In this report, we describe our theory and implementation of an efficient and effective method to correct for the main field and eddy current-induced direction-dependent distortions for DTI images under a unified framework to facilitate the daily practice of DTI acquisitions.
Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n=10, P<0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis.
Since changes in oxygen consumption induced by active neurons are specific to cortical columns, the small and transient "dip" of deoxyhemoglobin signal, which indicates an increase in oxygen consumption, has been of great interest. In this study, we succeeded in enhancing and sustaining the dip in the deoxyhemoglobin-weighted 620-nm intrinsic optical imaging signals from a 10-s orientation-selective stimulation in cat visual cortex by reducing arterial blood pressure with sodium nitroprusside (a vasodilator) to mitigate the contribution of stimulus-induced blood supply. During this condition, intact spiking activity and a significant reduction of stimulus-induced blood volume changes (570-nm intrinsic signals) were confirmed. The deoxyhemoglobin signal from the prolonged dip was highly localized to iso-orientation domains only during the initial approximately 2 s; the signal specificity weakened over time although the domains were still resolvable after 2 s. The most plausible explanation for this time-dependent spatial specificity is that deoxyhemoglobin induced by oxygen consumption drains from active sites, where spiking activity occurs, to spatially non-specific downstream vessels over time. Our results suggest that the draining effect of pial and intracortical veins in dHb-based imaging techniques, such as blood oxygenation-level dependent (BOLD) functional MRI, is intrinsically unavoidable and reduces its spatial specificity of dHb signal regardless of whether the stimulus-induced blood supply is spatially specific.