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PubMed2005

Optical computer recognition of facial expressions associated with stress induced by performance demands.

Application of computer vision to track changes in human facial expressions during long-duration spaceflight may be a useful way to unobtrusively detect the presence of stress during critical operations. To develop such an approach, we applied optical computer recognition (OCR) algorithms for detecting facial changes during performance while people experienced both low- and high-stressor performance demands. Workload and social feedback were used to vary performance stress in 60 healthy adults (29 men, 31 women; mean age 30 yr). High-stressor scenarios involved more difficult performance tasks, negative social feedback, and greater time pressure relative to low workload scenarios. Stress reactions were tracked using self-report ratings, salivary cortisol, and heart rate. Subjects also completed personality, mood, and alexithymia questionnaires. To bootstrap development of the OCR algorithm, we had a human observer, blind to stressor condition, identify the expressive elements of the face of people undergoing high- vs. low-stressor performance. Different sets of videos of subjects' faces during performance conditions were used for OCR algorithm training. Subjective ratings of stress, task difficulty, effort required, frustration, and negative mood were significantly increased during high-stressor performance bouts relative to low-stressor bouts (all p < 0.01). The OCR algorithm was refined to provide robust 3-d tracking of facial expressions during head movement. Movements of eyebrows and asymmetries in the mouth were extracted. These parameters are being used in a Hidden Markov model to identify high- and low-stressor conditions. Preliminary results suggest that an OCR algorithm using mouth and eyebrow regions has the potential to discriminate high- from low-stressor performance bouts in 75-88% of subjects. The validity of the workload paradigm to induce differential levels of stress in facial expressions was established. The paradigm also provided the basic stress-related facial expressions required to establish a prototypical OCR algorithm to detect such changes. Efforts are underway to further improve the OCR algorithm by adding facial touching and automating application of the deformable masks and OCR algorithms to video footage of the moving faces as a prelude to blind validation of the automated approach.

Adult↗
PubMed2000

Computer-based three-dimensional visualization of developmental gene expression.

A broad understanding of the relationship between gene activation, pattern formation and morphogenesis will require adequate tools for three-dimensional and, perhaps four-dimensional, representation and analysis of molecular developmental processes. We present a novel, computer-based method for the 3D visualization of embryonic gene expression and morphological structures from serial sections. The information from these automatically aligned 3D reconstructions exceeds that from single-section and whole-mount visualizations of in situ hybridizations. In addition, these 3D models of gene-expression patterns can become a central component of a future developmental database designed for the collection and presentation of digitized, morphological and gene-expression data. This work is accompanied by a web site (http://www.univie.ac.at/GeneEMAC).

Anatomy, Cross-Sectional↗
PubMedDate not supplied

Tauberian-prony feature extraction technique for esophageal motility patterns.

For the esophageal contractile activity recorded during swallowing, a feature extraction scheme has been developed. It recognizes the time, duration, and amplitudes of local peaks for each peristaltic wave. The method is based on the Tauberian approximation for modeling waveforms as a sum of identically shaped pulses with different time delays and amplitudes. Initial conditions on the pulse properties are set and an optimal solution is sought. The method is completely automated and can be utilized for characterization and classification purposes.

Algorithms↗
PubMed2003

Hierarchical active shape models, using the wavelet transform.

Active shape models (ASMs) are often limited by the inability of relatively few eigenvectors to capture the full range of biological shape variability. This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform. The statistical properties of the wavelet transform of a deformable contour are analyzed via principal component analysis, and used as priors in the contour's deformation. Some of these priors reflect relatively global shape characteristics of the object boundaries, whereas, some of them capture local and high-frequency shape characteristics and, thus, serve as local smoothness constraints. This formulation achieves two objectives. First, it is robust when only a limited number of training samples is available. Second, by using local statistics as smoothness constraints, it eliminates the need for adopting ad hoc physical models, such as elasticity or other smoothness models, which do not necessarily reflect true biological variability. Examples on magnetic resonance images of the corpus callosum and hand contours demonstrate that good and fully automated segmentations can be achieved, even with as few as five training samples.

Algorithms↗
PubMedDate not supplied

Computational methods for analysis of human breast tumor tissue in optical coherence tomography images.

Optical coherence tomography (OCT) has been demonstrated as a promising means of identifying the boundaries between normal and diseased breast tissue. This capability has yielded promise for the development of OCT techniques for biopsy guidance, surgical margin assessment, and minimally invasive evaluation of disease states. We present methods for the assessment of human breast tissue based on spatial and Fourier-domain analysis. Derived from preliminary OCT data, these methods are aimed at the development of automated diagnostic tools that will aid in the translation of this technology into the clinical environment.

Algorithms↗
PubMed2005

Computer-aided detection of breast masses on full field digital mammograms.

We are developing a computer-aided detection (CAD) system for breast masses on full field digital mammographic (FFDM) images. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multiresolution preprocessing scheme for image enhancement. A two-stage prescreening method that combines gradient field analysis with gray level information was developed to identify mass candidates on the processed images. The suspicious structure in each identified region was extracted by clustering-based region growing. Morphological and spatial gray-level dependence texture features were extracted for each suspicious object. Stepwise linear discriminant analysis (LDA) with simplex optimization was used to select the most useful features. Finally, rule-based and LDA classifiers were designed to differentiate masses from normal tissues. Two data sets were collected: a mass data set containing 110 cases of two-view mammograms with a total of 220 images, and a no-mass data set containing 90 cases of two-view mammograms with a total of 180 images. All cases were acquired with a GE Senographe 2000D FFDM system. The true locations of the masses were identified by an experienced radiologist. Free-response receiver operating characteristic analysis was used to evaluate the performance of the CAD system. It was found that our CAD system achieved a case-based sensitivity of 70%, 80%, and 90% at 0.72, 1.08, and 1.82 false positive (FP) marks/image on the mass data set. The FP rates on the no-mass data set were 0.85, 1.31, and 2.14 FP marks/image, respectively, at the corresponding sensitivities. This study demonstrated the usefulness of our CAD techniques for automated detection of masses on FFDM images.

Algorithms↗
PubMed2006

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

Algorithms↗
PubMed2006

Automated extraction of fine features of kinetochore microtubules and plus-ends from electron tomography volume.

Kinetochore microtubules (KMTs) and the associated plus-ends have been areas of intense investigation in both cell biology and molecular medicine. Though electron tomography opens up new possibilities in understanding their function by imaging their high-resolution structures, the interpretation of the acquired data remains an obstacle because of the complex and cluttered cellular environment. As a result, practical segmentation of the electron tomography data has been dominated by manual operation, which is time consuming and subjective. In this paper, we propose a model-based automated approach to extracting KMTs and the associated plus-ends with a coarse-to-fine scale scheme consisting of volume preprocessing, microtubule segmentation and plus-end tracing. In volume preprocessing, we first apply an anisotropic invariant wavelet transform and a tube-enhancing filter to enhance the microtubules at coarse level for localization. This is followed with a surface-enhancing filter to accentuate the fine microtubule boundary features. The microtubule body is then segmented using a modified active shape model method. Starting from the segmented microtubule body, the plus-ends are extracted with a probabilistic tracing method improved with rectangular window based feature detection and the integration of multiple cues. Experimental results demonstrate that our automated method produces results comparable to manual segmentation but using only a fraction of the manual segmentation time.

Algorithms↗
PubMed1979

An automated microscope for quantitative cytology combining television image analysis and stage scanning microphotometry.

This paper describes an automated microscope developed for operation in conjunction with the Leyden Television Analysis System. It features automated control of magnification, illumination, movement of scanning stages, and fine focus. These functions are controlled by means of a microcomputer. This enables a flexible design and relieves the supervising computer of simple but time consuming tasks. The combination of an automated microscope and Leyden Television Analysis System provides a powerful tool in quantitative cytological research. The flexible design permits other microscopic functions to be added with relatively little effort.

Computers↗
PubMed2005

Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs.

RATIONALE AND OBJECTIVE: The aim of the study was to survey misfiled cases in a picture archiving and communication system environment at two hospitals and to demonstrate the potential usefulness of an automated patient recognition method for posteroanterior chest radiographs based on a template-matching technique designed to prevent filing errors. MATERIALS AND METHODS: We surveyed misfiled cases obtained from different modalities in one hospital for 25 months, and misfiled cases of chest radiographs in another hospital for 17 months. For investigating the usefulness of an automated patient recognition and identification method for chest radiographs, a prospective study has been completed in clinical settings at the latter hospital. RESULTS: The total numbers of misfiled cases for different modalities in one hospital and for chest radiographs in another hospital were 327 and 22, respectively. The misfiled cases in the two hospitals were mainly the result of human errors (eg, incorrect manual entries of patient information, incorrect usage of identification cards in which an identification card for the previous patient was used for the next patient's image acquisition). The prospective study indicated the usefulness of the computerized method for discovering misfiled cases with a high performance (ie, an 86.4% correct warning rate for different patients and 1.5% incorrect warning rate for the same patients). CONCLUSION: We confirmed the occurrence of misfiled cases in the two hospitals. The automated patient recognition and identification method for chest radiographs would be useful in preventing wrong images from being stored in the picture archiving and communication system environment.

Database Management Systems↗
PubMed2006

Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method.

BACKGROUND: Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness), hence leaving a serious bottleneck. In this paper we propose a method for automatically acquiring knowledge to shorten the pattern list by locating the novel and interesting ones. METHODS: The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures the degree of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance. RESULTS: We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database and a biomedical literature citation knowledgebase. The system estimated association scores for 50,000 patterns, composed of disease entities and lab results, by querying the database and the knowledgebase. It then computed the surprise scores by comparing the pairs of association scores. Finally, the system estimated statistical significance of the scores. CONCLUSION: The dual-mining method eliminates more than 90% of patterns with strong associations, thus identifying them as uninteresting. We found that the pruning of patterns using the surprise score matched the biomedical evidence in the 100 cases that were examined by hand. The method automates the acquisition of knowledge, thus reducing dependence on the knowledge elicited from human expert, which is usually a rate-limiting step.

Algorithms↗
PubMed2005

Inter-operator variability in perfusion assessment of tumors in MRI using automated AIF detection.

A method is presented for the calculation of perfusion parameters in dynamic contrast enhanced MRI. This method requires identification of enhancement curves for both tumor tissue and plasma. Inter-operator variability in the derived rate constant between plasma and extra-cellular extra-vascular space is assessed in both canine and human subjects using semi-automated tumor margin identification with both manual and automated arterial input function (AIF) identification. Experimental results show a median coefficient of variability (CV) for parameter measurement with manual AIF identification of 21.5% in canines and 11% in humans, with a median CV for parameter measurement with automated AIF identification of 6.7% in canines and 6% in humans.

Animals↗
PubMed2005

Level set based cerebral vasculature segmentation and diameter quantification in CT angiography.

A level set based method is presented for cerebral vascular tree segmentation from computed tomography angiography (CTA) data. The method starts with bone masking by registering a contrast enhanced scan with a low-dose mask scan in which the bone has been segmented. Then an estimate of the background and vessel intensity distributions is made based on the intensity histogram which is used to steer the level set to capture the vessel boundaries. The relevant parameters of the level set evolution are optimized using a training set. The method is validated by a diameter quantification study which is carried out on phantom data, representing ground truth, and 10 patient data sets. The results are compared to manually obtained measurements by two expert observers. In the phantom study, the method achieves similar accuracy as the observers, but is unbiased whereas the observers are biased, i.e., the results are 0.00+/-0.23 vs. -0.32+/-0.23 mm. Also, the method's reproducibility is slightly better than the inter-and intra-observer variability. In the patient study, the method is in agreement with the observers and also, the method's reproducibility -0.04+/-0.17 mm is similar to the inter-observer variability 0.06+/-0.17 mm. Since the method achieves comparable accuracy and reproducibility as the observers, and since the method achieves better performance than the observers with respect to ground truth, we conclude that the level set based vessel segmentation is a promising method for automated and accurate CTA diameter quantification.

Algorithms↗
PubMed2004

Bidirectional imaging and modeling of skin texture.

In this paper, we present a method of skin imaging called bidirectional imaging that captures significantly more properties of appearance than standard imaging. The observed structure of the skin's surface is greatly dependent on the angle of incident illumination and the angle of observation. Specific protocols to achieve bidirectional imaging are presented and used to create the Rutgers Skin Texture Database (clinical component). This image database is the first of its kind in the dermatology community. Skin images of several disorders under multiple controlled illumination and viewing directions are provided publicly for research and educational use. Using this skin texture database, we employ computational surface modeling to perform automated skin texture classification. The classification experiments demonstrate the usefulness of the modeling and measurement methods.

Algorithms↗
PubMed2004

Computerized nipple identification for multiple image analysis in computer-aided diagnosis.

Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer. The nipple is a reliable and stable landmark on mammograms for the registration of multiple mammograms. However, accurate identification of nipple location on mammograms is challenging because of the variations in image quality and in the nipple projections, resulting in some nipples being nearly invisible on the mammograms. In this study, we developed a computerized method to automatically identify the nipple location on digitized mammograms. First, the breast boundary was obtained using a gradient-based boundary tracking algorithm, and then the gray level profiles along the inside and outside of the boundary were identified. A geometric convergence analysis was used to limit the nipple search to a region of the breast boundary. A two-stage nipple detection method was developed to identify the nipple location using the gray level information around the nipple, the geometric characteristics of nipple shapes, and the texture features of glandular tissue or ducts which converge toward the nipple. At the first stage, a rule-based method was designed to identify the nipple location by detecting significant changes of intensity along the gray level profiles inside and outside the breast boundary and the changes in the boundary direction. At the second stage, a texture orientation-field analysis was developed to estimate the nipple location based on the convergence of the texture pattern of glandular tissue or ducts towards the nipple. The nipple location was finally determined from the detected nipple candidates by a rule-based confidence analysis. In this study, 377 and 367 randomly selected digitized mammograms were used for training and testing the nipple detection algorithm, respectively. Two experienced radiologists identified the nipple locations which were used as the gold standard. In the training data set, 301 nipples were positively identified and were referred to as visible nipples. Seventy six nipples could not be positively identified and were referred to as invisible nipples. The radiologists provided their estimation of the nipple locations in the latter group for comparison with the computer estimates. The computerized method could detect 89.37% (269/301) of the visible nipples and 69.74% (53/76) of the invisible nipples within 1 cm of the gold standard. In the test data set, 298 and 69 of the nipples were classified as visible and invisible, respectively. 92.28% (275/298) of the visible nipples and 53.62% (37/69) of the invisible nipples were identified within 1 cm of the gold standard. The results demonstrate that the nipple locations on digitized mammograms can be accurately detected if they are visible and can be reasonably estimated if they are invisible. Automated nipple detection will be an important step towards multiple image analysis for CAD.

Algorithms↗
PubMed2005

Automated detection of elementary calcium release events using the á trous wavelet transform.

We developed an algorithm for the automated detection and analysis of elementary Ca2+ release events (ECRE) based on the two-dimensional nondecimated wavelet transform. The transform is computed with the "à trous" algorithm using the cubic B-spline as the basis function and yields a multiresolution analysis of the image. This transform allows for highly efficient noise reduction while preserving signal amplitudes. ECRE detection is performed at the wavelet levels, thus using the whole spectral information contained in the image. The algorithm was tested on synthetic data at different noise levels as well as on experimental data of ECRE. The noise dependence of the statistical properties of the algorithm (detection sensitivity and reliability) was determined from synthetic data and detection parameters were selected to optimize the detection of experimental ECRE. The wavelet-based method shows considerably higher detection sensitivity and less false-positive counts than previously employed methods. It allows a more efficient detection of elementary Ca2+ release events than conventional methods, in particular in the presence of elevated background noise levels. The subsequent analysis of the morphological parameters of ECRE is reliably reproduced by the analysis procedure that is applied to the median filtered raw data. Testing the algorithm more rigorously showed that event parameter histograms (amplitude, rise time, full duration at half-maximum, and full width at half-maximum) were faithfully extracted from synthetic, "in-focus" and "out-of-focus" line scan sparks. Most importantly, ECRE obtained with laser scanning confocal microscopy of chemically skinned mammalian skeletal muscle fibers could be analyzed automatically to reproducibly establish event parameter histograms. In summary, our method provides a new valuable tool for highly reliable automated detection of ECRE in muscle but can also be adapted to other preparations.

Algorithms↗
PubMedDate not supplied

Semisupervised learning for molecular profiling.

Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. Researchers have become aware how easy it is to incur a selection bias effect, and complex validation setups are required to avoid overly optimistic estimates of the predictive accuracy of the models and incorrect gene selections. This paper describes a semisupervised pattern discovery approach that uses the by-products of complete validation studies on experimental setups for gene profiling. In particular, we introduce the study of the patterns of single sample responses (sample-tracking profiles) to the gene selection process induced by typical supervised learning tasks in microarray studies. We originate sample-tracking profiles as the aggregated off-training evaluation of SVM models of increasing gene panel sizes. Genes are ranked by E-RFE, an entropy-based variant of the recursive feature elimination for support vector machines (RFE-SVM). A Dynamic Time Warping (DTW) algorithm is then applied to define a metric between sample-tracking profiles. An unsupervised clustering based on the DTW metric allows automating the discovery of outliers and of subtypes of different molecular profiles. Applications are described on synthetic data and in two gene expression studies.

Algorithms↗
PubMed2005

Automated DNA fragments recognition and sizing through AFM image processing.

This paper presents an automated algorithm to determine DNA fragment size from atomic force microscope images and to extract the molecular profiles. The sizing of DNA fragments is a widely used procedure for investigating the physical properties of individual or protein-bound DNA molecules. Several atomic force microscope (AFM) real and computer-generated images were tested for different pixel and fragment sizes and for different background noises. The automated approach minimizes processing time with respect to manual and semi-automated DNA sizing. Moreover, the DNA molecule profile recognition can be used to perform further structural analysis. For computer-generated images, the root mean square error incurred by the automated algorithm in the length estimation is 0.6% for a 7.8 nm image pixel size and 0.34% for a 3.9 nm image pixel size. For AFM real images we obtain a distribution of lengths with a standard deviation of 2.3% of mean and a measured average length very close to the real one, with an error around 0.33%.

Algorithms↗