False Features Research Articles

Contrast meals and malrotation in children—metal markers for improved accuracy

Upper gastrointestinal contrast studies in children may cause false-positive or -negative diagnosis of intestinal malrotation from rotation of the patient. To alleviate this problem, skin markers can be used to reduce rotation of children undergoing this procedure, e.g., two metal markers (sheathed and sealed hypodermic needles) can be fixed onto the skin for gastro-intestinal contrast studies. We reviewed two Katz criteria influenced by patient rotation: duodenojejunal junction on or to the right of the left pedicle and pylorus to the left of the midline. A test group was positioned using markers; a control group without markers was positioned conventionally. Markers during a pilot study were applied, but positioning was done by helpers who had no on-screen visualization. In the test group, only 1 child (3%; n = 39) had a feature of malrotation. In the control group, there were features of malrotation in 12 children (25%; n = 48). No other features of malrotation were seen. The pilot study showed radiographic rotation with markers projecting off the midline in 78% of 58 children. This resulted in 48% of 58 patients having false features of malrotation. The use of metal skin markers results in reduction of rotational errors that could have caused false diagnosis of intestinal malrotation in children.

Fingerprint images segmentation based on fuzzy C-mean theory and statistical features

Fingerprint segmentation is a crucial and important step of image processing in automatic fingerprint identification. Because, it is very important for alright fingerprint features extraction, such as, singular points, bifurcation and ridge ending minutia’s. The aim of the segmentation of fingerprint is to extract the interest area (foreground) and to exclude the background regions, in order to reduce the time of subsequent processing and to avoid detecting false features. This paper presents a new approach of fingerprints segmentation. This approach is based on variance image and combined fuzzy C-mean algorithm with the statistical features. Fingerprint segmentation results from the proposed method are validated and the accuracy of segmentation sensitivity for the test data available is evaluated. We have tested this technique on more than 1000 images fingerprint taken from “CASIA Fingerprint Image Database Version 5.0” (CASIA-FingerprintV5). Then a comparative study with the existing techniques is presented. The experimental results demonstrate the superiority, the effectiveness and the robustness of the proposed method. Key words: Fuzzy C-means, biometrics, fingerprint image, segmentation, statistical features.

A review of false memory features and conditions of its reduction among the elderly

A review of false memory features and conditions of its reduction among the elderly

A Bayesian calibration model for combining different pre-processing methods in Affymetrix chips

BackgroundIn gene expression studies a key role is played by the so called "pre-processing", a series of steps designed to extract the signal and account for the sources of variability due to the technology used rather than to biological differences between the RNA samples. At the moment there is no commonly agreed gold standard pre-processing method and each researcher has the responsibility to choose one method, incurring the risk of false positive and false negative features arising from the particular method chosen.ResultsWe propose a Bayesian calibration model that makes use of the information provided by several pre-processing methods and we show that this model gives a better assessment of the 'true' unknown differential expression between two conditions. We demonstrate how to estimate the posterior distribution of the differential expression values of interest from the combined information.ConclusionOn simulated data and on the spike-in Latin Square dataset from Affymetrix the Bayesian calibration model proves to have more power than each pre-processing method. Its biological interest is demonstrated through an experimental example on publicly available data.

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data.

BackgroundDesigning appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal.ResultsA hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases.ConclusionMathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently.

Exploration of faint absorption bands in the reflectance spectra of the asteroids by method of optimal smoothing: Vestoids

An optimization method of smoothing noisy spectra was developed to investigate faint absorption bands in the visual spectral region of reflectance spectra of asteroids and the compositional information derived from their analysis. The smoothing algorithm is called “optimal” because the algorithm determines the best running box size to separate weak absorption bands from the noise. The method is tested for its sensitivity to identifying false features in the smoothed spectrum, and its correctness of forecasting real absorption bands was tested with artificial spectra simulating asteroid reflectance spectra. After validating the method we optimally smoothed 22 vestoid spectra from SMASS1 [Xu, Sh., Binzel, R.P., Burbine, T.H., Bus, S.J., 1995. Icarus 115, 1–35]. We show that the resulting bands are not telluric features. Interpretation of the absorption bands in the asteroid spectra was based on the spectral properties of both terrestrial and meteorite pyroxenes. The bands located near 480, 505, 530, and 550 nm we assigned to spin-forbidden crystal field bands of ferrous iron, whereas the bands near 570, 600, and 650 nm are attributed to the crystal field bands of trivalent chromium and/or ferric iron in low-calcium pyroxenes on the asteroids' surface. While not measured by microprobe analysis, Fe 3+ site occupancy can be measured with Mössbauer spectroscopy, and is seen in trace amounts in pyroxenes. We believe that trace amounts of Fe 3+ on vestoid surfaces may be due to oxidation from impacts by icy bodies. If that is the case, they should be ubiquitous in the asteroid belt wherever pyroxene absorptions are found. Pyroxene composition of four asteroids of our set is determined from the band position of absorptions at 505 and 1000 nm, implying that there can be orthopyroxenes in all range of ferruginosity on the vestoid surfaces. For the present we cannot unambiguously interpret of the faint absorption bands that are seen in the spectra of 4005 Dyagilev, 4038 Kristina, 4147 Lennon, and 5143 Heracles. Probably there are other spectrally active materials along with pyroxenes on the surfaces of these asteroids.

Two steps for fingerprint segmentation

A fingerprint image usually consists of different regions: non-ridge regions, high quality ridge regions, and low quality ridge regions. Fingerprint segmentation is usually to exclude non-ridge regions and unrecoverable low quality ridge regions as background so as to avoid detecting false features. In ridge regions, including high quality and low quality, there are often some remaining ridges which are the afterimage of the previously scanned finger and are expected to be excluded as background. However, existing segmentation methods do not take this case into consideration, and often, the remaining ridge regions are falsely taken as foreground. This paper proposes two steps for fingerprint segmentation to exclude the remaining ridge region from the foreground. The non-ridge regions and unrecoverable low quality ridge regions are removed as background in the first step, and then the foreground produced by the first step is further analyzed so as to remove the remaining ridge region. The experimental results showed the effectiveness of the proposed method in segmenting the remaining ridges as background and in turn producing much less spurious minutiae than the existing method.

Removing the remaining ridges in fingerprint segmentation

Fingerprint segmentation is an important step in fingerprint recognition and is usually aimed to identify non-ridge regions and unrecoverable low quality ridge regions and exclude them as background so as to reduce the time expenditure of image processing and avoid detecting false features. In high and in low quality ridge regions, often are some remaining ridges which are the afterimages of the previously scanned finger and are expected to be excluded from the foreground. However, existing segmentation methods generally do not take the case into consideration, and often, the remaining ridge regions are falsely classified as foreground by segmentation algorithm with spurious features produced erroneously including unrecoverable regions as foreground. This paper proposes two steps for fingerprint segmentation aimed at removing the remaining ridge region from the foreground. The non-ridge regions and unrecoverable low quality ridge regions are removed as background in the first step, and then the foreground produced by the first step is further analyzed for possible remove of the remaining ridge region. The proposed method proved effective in avoiding detecting false ridges and in improving minutiae detection.

Test of fire detection algorithms using artificially generated events

The paper proposes a test method that is particularly suitable for the test of the false alarm rejection features of detection algorithms. It describes how to generate signal events artificially, which are similar to those occurring in practice, and thus may or may not cause false or unwanted alarms. The generated events are of random nature, i.e. the signal shape, amplitude and duration of the events is randomly varied and the points in time, where these events occur, are randomly selected according to a well defined signal model. A set of parameters controls the event density, the probability for the duration of events, the fluctuation and the full dynamic range. The whole signal model uses only one pseudo-random generator and generates a modified “random walk process”. Thus, it is either possible to reproduce with the same set of parameters for the signal model the same random sequence of events or, alternatively, to generate different random signal sequences using a different seed for the pseudo-random generator. The test procedure is carried out in the form of a computer simulation in time-lapse mode, and thus requires the detection algorithm to be available as a C-program.

Segmentation of Fingerprint Images Using Linear Classifier

An algorithm for the segmentation of fingerprints and a criterion for evaluating the block feature are presented. The segmentation uses three block features: the block clusters degree, the block mean information, and the block variance. An optimal linear classifier has been trained for the classification per block and the criteria of minimal number of misclassified samples are used. Morphology has been applied as postprocessing to reduce the number of classification errors. The algorithm is tested on FVC2002 database, only 2.45% of the blocks are misclassified, while the postprocessing further reduces this ratio. Experiments have shown that the proposed segmentation method performs very well in rejecting false fingerprint features from the noisy background.

Automated model-based organ delineation for radiation therapy planning in the prostate region

Purpose/Objective: Organ delineation is currently the most time-consuming part in radiation therapy planning (RTP). This procedure is usually performed by manual contouring in 2-D slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a 3-D dataset. Recent transition from conformal methods to Intensity Modulated Radiation Therapy (IMRT) and increasing interest in 4D radiotherapy, which takes changes in the patient’s anatomy into account, increase the burden of organ delineation considerably. The development of fast and robust automated segmentation tools is crucial for these novel treatment methods to succeed.

On the importance of resolving the western boundary layer in wind-driven ocean general circulation models

The western boundary layer (WBL) plays a fundamental role in basin-scale wind-driven ocean circulations. In idealized ocean models with flat bottom topography, this layer is required not only to balance the interior Sverdrup transport to close the gyre circulation, but also to dissipate the vorticity imposed by the wind-stress curl. The width of the WBL in Munk-type models is estimated to be δ M( A H/ β) 1/3, where A H and β are horizontal eddy viscosity and the meridional derivative of the Coriolis parameter respectively. For commonly used values of A H, the boundary-layer width δ M ranges from 30 to less than 200 km in the mid-latitude ocean. This scale is often poorly resolved in large-scale climate models. This paper intends to demonstrate some consequences when the western boundary layer is not adequately resolved. It is found that coarse resolution models reach equilibrium states by distorting some important dynamics in order to dissipate wind-imposed vorticity. In three-dimensional models, for instance, very strong spurious upwelling and downwelling can occur along the WBL. In models of two-dimensional flow, however, spurious recirculations may develop near the boundary. These false features can be removed when the boundary layer is better resolved. We propose a method in which a spatially varying A H is used to broaden the WBL without affecting mixing in the interior. The method improves the model results considerably.

Hybrid mapping in VLBI and spurious symmetrization

The standard procedure used in hybrid map- ping of VLBI data is examined. That this procedure can converge on wrong solutions has been pointed in the past by many authors. Walker (1996) indicated the bias in the resultant data due to the use of a point source as start- ing model. B a ath (1989) suggested the use of the original data set in each iteration of self calibration, and not the use of the corrected one created by the previous iteration. Lineld (1986) analyzed the role of the u v coverage and lack of intermediate spacings. However, the full pro- cedure needed to avoid false features is at present not clear and only experience with imaging helps the user to avoid them. In this paper we demonstrate that the problem is connected with the non-linear nature of self calibration which leaves initial wrong assumptions frozen in the nal solution. We demonstrate how large closure phase values linearize the process and we show a simple modication of the standard procedure which helps to avoid creation of false features in the imaging.

Airborne resistivity data leveling

Helicopter‐borne frequency‐domain electromagnetic (EM) data are used routinely to produce resistivity maps for geologic mapping, mineral exploration, and environmental investigations. The integrity of the resistivity data depends in large part on the leveling procedures. Poor resistivity leveling procedures may, in fact, generate false features as well as eliminate real ones. Resistivity leveling is performed on gridded data obtained by transformation of the leveled EM channel data. The leveling of EM channel data is often imperfect, which is why the resistivity grids need to be leveled. We present techniques for removing the various types of resistivity leveling errors which may exist. A semi‐automated leveling technique uses pseudo tie‐lines to remove the broad flight‐based leveling errors and any high‐magnitude line‐based errors. An automated leveling technique employs a combination of 1-D and 2-D nonlinear filters to reject the rest of the leveling errors including both long‐and short‐wavelength leveling errors. These methods have proven to be useful for DIGHEM helicopter EM survey data. However, caution needs to be exercised when using the automated technique because it cannot distinguish between geological features parallel to the flight lines and leveling errors of the same wavelength. Resistivity leveling is not totally objective since there are no absolutes to the measured frequency‐domain EM data. The fundamental integrity of the EM data depends on calibration and the estimate of the EM zero levels. Zero level errors can be troublesome because there is no means by which the primary field can be determined absolutely and therefore subtracted to yield an absolute measure of the earth’s response. The transform of incorrectly zero‐leveled EM channels will yield resistivity leveling errors. Although resistivity grids can be leveled empirically to provide an esthetically pleasing map, this is insufficient because the leveling must also be consistent across all frequencies to allow resistivity to be portrayed in section. Generally, when the resistivity looks correct in plan and section, it is assumed to be correct.

Vibration Technique for Locating Delamination in a Composite Beam

An experimental nondestructive vibration-based technique for locating a delamination in a composite beam is presented. The method operates on the fundamental displacement eigenvector, which is converted to a curvature mode shape. The application of a unique, gapped smoothing damage detection method to the curvature yields a damage index that locates the delamination, irrespective of its position along the beam or depth within the beam. The procedure can operate solely on data obtained from the damaged structure. Models or data from the undamaged structure are specifically not required or used during the analysis. The procedure reported here is highly satisfactory for experimental data, where small variations in the measurement cause false features in other published curvature-based damage detection methods. The damage location method is demonstrated with a finite element model of a composite beam, where a delamination is modeled by relaxing connectivity between elements at the desired location of the delamination. The results of an experimental investigation of a composite beam with a manufactured delamination are also presented. When the gapped smoothing method was used on the experimental modal data, the delamination was successfully located.

Finger tracking for breast palpation quantification using color image features

A vision-based finger motion tracking approach is presented to gather quantitative data about breast palpation for cancer detection, such as finger positions, search pattern and coverage area, and this approach is used to develop a prototype palpation training system. Special color markers are used as features of interest because in breast palpation the background of the image is the breast itself which is similar to the fingers in color. This color similarity can hinder the ability or efficiency of other feature extraction methods if real-time performance is required. To simplify the feature extraction process, color space transform is utilized instead of directly using the original RGB values of the image. Although the clinical environment will be well illuminated, normalization of color attributes is applied to compensate for minor changes in illumination. A neighbor search is employed to ensure real-time performance, and a three-finger pattern topology is checked for the extracted features to avoid any possible false features. After detecting the features in the images, 3-D positions of the color marked fingers are calculated using the stereo vision principle. Experimental results with the prototype training system are given to show the performance and effectiveness of the proposed approach. This approach is expected to significantly improve the training quality of breast palpation, thus increasing the detection rate and accuracy of breast cancer.

THE SEISMIC EXPRESSION OF REAL AND APPARENT BURIED TOPOGRAPHY

Subaerial, and in some case submarine, erosional processes sometimes leave a depositional surface which is not plane. During any subsequent transgression and sedimentation, remnant relief on the old surface may be preserved, particularly where depositional rates during the early period of the transgression are high, and the environment energy of deposition is relatively low. As well as in circumstances involving normal clatic sediments, similar situtions can arise in the inundation of a volcanic surface including relief features like vents, fissures and mounds, or of a karstified surface‐carbonate unit.Apparent, or “false” buried topography can be produced by a wide range of geological processes. The difference between such features and those of true buried topography is that at the time of transgression, the latter formed part of the contempory Earth surface, whilst in the case of the false features, the surface at which they were formed was already buried beneath varying depths of sediment.Attention has been directed in recent years to the potential importance of buried topographic features in hydrocarbon exploration. From this viewpoint, the difference between true and false features of this type may be trivial in some cases, but important in others, usually because of the details of the sedimentological processes involved.

Clinical diagnosis of abdominal tuberculosis.

One hundred and eighty-two cases of abdominal tuberculosis admitted to Swaroop Rani Nehri (SRN) Hospital, Allahabad, in the past 7 years have been reviewed. The clinical diagnosis of abdominal tuberculosis was made correctly only in 50 per cent of cases. About half the cases presented with chronic or acute on chronic intestinal obstruction. The remaining patients had vague pains, tender abdomen, constitutional symptoms or a mass in the abdomen. Diarrhoea was not frequent and fistula formation was rare. A chronic obstructive type of lesion was found not only in cases with a bowel lesion but also in patients with chronic miliary peritonitis and tuberculous mesenteric adenitis. Similarly, a lump was present not only in hypertrophic bowel tuberculosis but also in chronic miliary peritonitis and tuberculous mesenteric adenitis. On radiological examination false positive features such as fluid levels, bowel dilatation or even the 'string' sign were encountered. Liver and endometrial biopsies were positive in only a very few cases. Peritoneal biopsy was of considerable help, being positive in 88 per cent of ascitic cases and in 42-1 per cent of non-ascitic cases. Open peritoneal biopsy obtained after making a small incision in the right iliac fossa was found to be the most useful investigation in the diagnosis of abdominal tuberculosis. An ascitic fluid protein content of 2-5 g or more and a predominantly lymphocytic count of over 100/mm3 are diagnostic, but a cell count of 10/mm3 was recorded in one tuberculous case.

Speculations concerning some uses of gaining

Working with games leads to situations in which one's thinking is constantly tested by direct confrontation with a very wide variety of people. Games challenge people, but people also challenge games. Very few weeks go by without a specific challenge to my work from the social or pedagogical left on grounds that an emphasis on games exacerbates the already pronounced tendency for our society to be too competitive-and an attack from the right on grounds that games are trivial. But quite apart from those kinds of challenge there is the more direct challenge that comes from people who engage in playing games and declare that they are not as much fun as they anticipated, or that they contain false and misleading features.