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The primary purpose of this study was to investigate the feasibility of using simulated data from the United Kingdom Meteorological Office (UKMO) global climate mathematical model to serve as boundary values for a regional model RM3 which has been used by NASA to make predictions about climate dynamics in West Africa. In the past, historical data has been used successfully as boundary data but this approach limits outcomes to time periods in the past. The advantage of using the UKMO data is its potential to provide input boundary data for future time periods resulting in future regional predictions. This study has provided NASA scientists with graphical and statistical summaries including visual animations that provide qualitative and quantitative information necessary for evaluating whether the UKMO data can be used as a driving force for the RM3 model. One definite conclusion of this investigation is that both spatial and temporal interpolation of UKMO results will be necessary in order to make its results compatible with the RM3 model.

In this paper the ability of two common statistical discriminant analysis procedures are compared with two commercial neural network software packages. The major objective of this study was to determine which of the procedures could best discriminate between normal and abnormal ultrasonic liver textures. The same set of features were input into both statistical discriminant analysis procedures and both neural network models. Preliminary results have found the restricted Coulomb Energy (RCE) neural network model to have a testing accuracy of 90.6% which is approximately 10% better than any of the other techniques investigated. © 1991.

The primary objective of this project is to define a methodology to depict the motion of deep convective cloud systems as observed form satellite imagery. These clouds are defined as clusters of pixels with Cloud Top Pressure (IPC) <EQ 440 millibars and Cloud Optical Thickness (TAU) >= 23 which are high in the atmosphere and sufficiently thick to produce significant rainfall. Clouds are one of the major factors in understanding the earth's climate. Evaluating cloud motion is important in understanding atmospheric dynamics and visualizations are vital because they provide a good way to observe change. IPC and TAU values have been collected for April of 1989 from the International Satellite Cloud Climatology Project, low resolution database for the northern latitudes between 30 and 60 degrees. Each of the 240 IPC and 240 TAU images consisted of 12 rows and 144 columns with each pixel representing a 280 km square on the globe collected in three-hour intervals. Individual images were color coded according to land, sea and clouds before being put into motion. Six animations have been produced which start with the original images, progress to include daily composite images and culminate with a collage. Animations of the original images have the advantage of relatively short intervals between still frames but have many undefined pixels, which are eliminated in the composites. The results of this project can serve as an example of how to improve the visualization of time varying image sequences.

Recent developments in image digitization have made possible a more quantitative analysis of ultrasonic imagery of the liver, which could lead to a more sensitive method for changes in liver texture as an aid in the diagnosis of liver disease. The approach described is the statistical analysis of one-dimensional intensity (gray-level) histograms obtained from B-mode ultrasonic images. First-order statistical parameters are used to characterize the location, variability, skewness and kurtosis of the histograms. One typical normal study and one typical abnormal study are presented to shown the type of results that have been obtained.

This paper describes a collaborative project conducted by the Computer Science Department at Southern Connecticut State University and NASA's Goddard Institute for Space Science (GISS). Animations of output from a climate simulation math model used at GISS to predict rainfall and circulation have been produced for West Africa from June to September 2002. These early results have assisted scientists at GISS in evaluating the accuracy of the RM3 climate model when compared to similar results obtained from satellite imagery. The results presented below will be refined to better meet the needs of GISS scientists and will be expanded to cover other geographic regions for a variety of time frames.

Nanoparticles are of interest in many applications since their decreased size may give them properties that are very different from bulk material. Often nanoparticle properties such as size (diameter) and size distribution are evaluated using transmission electron microscopy (TEM). These parameters, size and size distribution, can be more easily obtained from digitized TEM images by mapping particle signal to black and background pixel to white in a process known as thresholding then performing an algorithm known as a particle analysis. The goal of this study was to compare the ability of several popular thresholding algorithms to segment TEM images. Performance of the thresholding algorithms was evaluated through qualitative and quantitative measures. Results show that the choice of a thresholding algorithm will strongly affect the results obtained from particle analysis. © 2007 Materials Research Society.

In scientific imaging, it is crucial to obtain precise images to facilitate accurate observations for the given application. However, often times the imaging equipment used to acquire such images introduces error into the observed image. Therefore, there is a fundamental need to remove the error associated with these images in order to facilitate accurate observations. This study investigates the effectiveness of an image processing technique utilizing an iterative deconvolution algorithm to remove error from micro-CT images. This technique is applied to several sets of in-vivo micro CT scans of mice, and its effectiveness is evaluated by qualitative comparison of the resultant thresholded binary images to thresholded binary images produced by more conventional image processing techniques; namely Gaussian filtering and straight thresholding. Results for this study suggest that iterative deconvolution as a pre-processing step produces superior qualitative results as compared to the more conventional methods tested. The groundwork for future quantitative verification is motivated. ©2005 IEEE.

Backpropagation neural networks are applied to the problem of characterization of ultrasonic image texture to detect abnormalities in tissue texture which are indicative of liver disease. Twenty-one texture features were extracted from regions of interest in digitized ultrasonic images. A feature subset, identified by a stepwise selection process, formed the sample input to the networks together with the physician-supplied diagnosis. The classification performance of the backpropagation network is evaluated using a jackknife testing procedure. The performance of the networks is compared with results obtained from linear discriminant analysis and logistic regression techniques. © Springer-Verlag Berlin Heidelberg 1995.

While cooccurrence matrices have been shown to be helpful in quantitating image texture, the amount of data associated with them can rapidly become unmanageable because a separate cooccurrence matrix can be calculated for each displacement vector chosen. Here, a method for choosing the direction of the displacement vector that is based on the most dominant edge obtained from gradient analysis is discussed. Also, the anatomy of the liver is used to suggest the most important intersample spacing in constructing cooccurrence matrices for the evaluation of diffuse liver disease.

Accurate identification and tracking of synoptic-scale storm systems in the northern midlatitudes is important for understanding the structure and movement of the midlatitude cloud field which plays a major role in climate change. In this paper, a hybrid neural network/genetic algorithm (NN/GA) approach is presented that analyzes the behavior of storm systems from one time frame to the next. The goal of the hybrid neural network algorithm is to improve classifier output by reducing the number of infeasible solutions using constraint optimization techniques. The input to the hybrid neural network algorithm is the output from a traditional backpropagation neural network. The hybrid NN/GA analyzes the backpropagation neural network output for logical consistencies and makes changes to the classification results based on strength of neural network classifications and satisfaction of logical constraints. The results are compared with classification results obtained using linear discriminant analysis, k-nearest neighbor rule, and backpropagation neural network techniques.

The purpose of this study was to compare the classification capabilities of the backpropagation algorithm and linear discriminant analysis for detecting liver metastisis using image texture features obtained from ultrasonic images of the liver. Twenty-one quantitative parameters were obtained from 134 regions of interest of equal size. The images were collected by the same radiologist on the same imager with the controls adjusted for variations in patient body size so as to produce images of consistent quality. Quantitative features were divided so that 13 were first-order statistics, 6 were second-order statistics, and 2 were image gradient parameters. The same features were processed by both the backpropagation algorithm and linear discriminant analysis using `jack-knife'' testing and the results of each computer- generated classification was compared to the supplied diagnosis in an effort to determine which method could best identify patterns. For this particular application, the backpropagation neural network was found to have slightly superior classification results (87) than linear discriminant analysis (83).

The objective of this study is to compare statistical and unsupervised neural network techniques for determination of correspondences between storm system regions extracted from sequences of satellite images. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution D1 database for selected storm systems during the period April 5 - 9, 1989. Cloud top pressure was used to delineate regions of interest and cloud optical thickness combined with spatial location was used to track regions throughout a given time sequence. The ability of the k-nearest neighbor classifier and of self-organizing maps to determine correspondences between storm regions was assessed. The two techniques generally yielded similar associations between regions of interest throughout the time sequence. Differences in final tracking results between the two techniques occurred primarily as a result of differences in the collections of points from a region in a time step t<SUB>2</SUB> that corresponded to a region in an earlier time step t<SUB>1</SUB>. The tracking results were also compared to the results obtained at the NASA Goddard Institute for Space Studies using sea level pressure data from the National Meteorological Center (NMC). For the storm systems investigated in this study, the storm tracks exhibited the same general tracking behavior with expected variations between cloud system storm centers and low sea level pressure centers.

The purpose of this study was to compare the ability of several texture analysis parameters to differentiate textured samples from a smooth control on images obtained with an Atomic Force Microscope (AFM). Surface roughness plays a major role in the realm of material science, especially in integrated electronic devices. As these devices become smaller and smaller, new materials with better electrical properties are needed. New materials with smoother surface morphology have been found to have superior electrical properties than their rougher counterparts. Therefore, in many cases surface texture is indicative of the electrical properties that material will have. Physical vapor deposition techniques such as Jet Vapor Deposition and Molecular Beam Epitaxy are being utilized to synthesize these materials as they have been found to create pure and uniform thin layers. For the current study, growth parameters were varied to produce a spectrum of textured samples. The focus of this study was the image processing techniques associated with quantifying surface texture. As a result of the limited sample size, there was no attempt to draw conclusions about specimen processing methods. The samples were imaged using an AFM in tapping mode. In the process of collecting images, it was discovered that roughness data was much better depicted in the microscope's "height" mode as opposed to "equal area" mode. The AFM quantified the surface texture of each image by returning RMS roughness and the first order histogram statistics of mean roughness, standard deviation, skewness, and kurtosis. Color images from the AFM were then processed on an off line computer running NIH ImageJ with an image texture plug in. This plug in produced another set of first order statistics computed from each images' histogram as well as second order statistics computed from each images' cooccurrence matrix. The second order statistics, which were originally proposed by Haralick, include contrast, angular second moment, correlation, inverse difference moment, and entropy. These features were computed in the 0°, 45°, 90°, and 135° directions. The findings of this study propose that the best combination of quantitative texture parameters is standard deviation, 0° inverse difference moment, and 0° entropy, all of which are obtained from the NIH ImageJ texture plug in. © 2010 Copyright SPIE - The International Society for Optical Engineering.

There is an acute and well-documented need for image processing of microscopy data in materials science regarding, for example, the characterization of the structure/property relationship of a given materials system. In our work, image processing has been used as a framework for conducting interdisciplinary team-based research that effectively integrates programs within the Center for Research on Interface Structures and Phenomena (CRISP) Materials Research Science and Engineering Center (MRSEC), e.g. research experiences for undergraduates (REU), teachers (RET) and high school fellowships. This research resulted from a five-year long collaboration between CRISP and the Physics and Computer Science Departments at Southern Connecticut State University (SCSU). This paper will focus on the implementation of team-based research experiences as a vehicle for interdisciplinary science and education. Representative results of several of the studies are presented and discussed. © 2011 Materials Research Society.

Qualitative analysis is important because it is not subjective and does not have the potential for variation from one observer to another. A description is given of how statistical hypothesis testing can be used to select the quantitative descriptors best capable of distinguishing between normal and abnormal liver texture. Information is also presented on how both parametric and nonparametric discriminant analysis can be applied to determine how well the quantitative analysis compares with the qualitative diagnosis supplied for each case studied.

The primary objective of this research is the development and testing of neural network models for two fundamental computer vision tasks: edge/line detection and texture analysis. In order to test the ability of the neural network models to detect patterns in images we used both remote sensing data and medical imagery. Neural network models for edge and line detection were used to detect geological lineaments in Landsat data. Neural network models for the analysis of image texture variations were used on ultrasonic images to distinguish patients with normal liver scans from patients with diffuse liver disease. 1.

Temporal and spatial analysis was applied to a sequence of cloud top pressure (CTP) images and cloud optical thickness (TAU) images, and a storm tracking algorithm was proposed. A sequence of storm tracks from the satellite images was developed from the satellite images. Composite images were created by projecting ahead in time and substituting the first valid pixel for missing data, and a variety of CTP and TAU cut-off values were used to identify regions of interest. The region correspondences were determined from one time frame to another which yielded the storm center coordinates. The obtained tracks were compared to the storm tracks computed from sea level pressure data by matching the results first in time and then in spatial distance.

The objective of this study is to compare geometric-based and evolutionary techniques for tracking storm systems from sequences of satellite images. Analysis was applied to the International Satellite Cloud Climatology Project low resolution D1 database for selected storm systems during the month of September, 1988. During this time period there were two exceptionally long tracks of major hurricane systems, Hurricanes Gilbert and Helene. Cloud top pressure and cloud optical thickness were used to identify storm systems. The ability of the geometric-based and evolutionary techniques to generate tracks through storm regions was assessed. Differences in final tracking results between the two techniques resulted not only from the differences in methodology but also form differences in the type of preprocessed input used by each of the techniques. Tracking results were compared to results disseminated by the Colorado State/Tropical Prediction Center and maintained by the National Hurricane Center in Miami, Florida. For the hurricanes investigated in this study, both techniques were able to generate tracks which followed either most or some of the portions of the hurricanes. The evolutionary algorithm was in general able to maintain good continuity along the tracks but, with no knowledge of overall region movement, was unable to discern which of two possible directions would be best to pursue in cases where there were tow or more equally close storm systems components. The geometric method was able to maintain a smooth track close to the course of the hurricane except for confusion primarily at the beginning and/or end of tracks.

An evolutionary system was developed for generation of complete tracks of northern midlatitude synoptic-scale storm systems based on optical flow and cloud motion analyses of global satellite-based datasets produced by the International Satellite Cloud Climatology Project (ISCCP). The tracking results were compared with low sea level pressure anomaly (SLPA) tracks obtained from the NASA Goddard Institute for Space Studies (GISS). The SLPA tracks were produced at GISS by analysis of meteorological, ground-based National Center for Environmental Prediction (NCEP) datasets. Results from the evolutionary system were also compared with results from using (a) the k-nearest neighbor rule (k-NN) and (b) self-organizing maps (SOM) to determine correspondences between consecutive locations within a track. The consistency of our evolutionary storm tracking results with the behavior of the low sea level pressure anomaly tracks, the ability of our evolutionary system to generate and evaluate complete tracks, and the close comparison between the results obtained by the evolutionary, k-NN, and SOM analyses of the ISCCP-derived datasets at tracking steps in which proximity or optical flow information sufficed to determine movement, demonstrate the applicability and the potential of evolutionary systems for tracking midlatitude storm systems through low-resolution ISCCP cloud product datasets.