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PubMed2003

Remote detection of heated ethanol plumes by airborne passive Fourier transform infrared spectrometry.

Methodology is developed for the automated detection of heated plumes of ethanol vapor with airborne passive Fourier transform infrared spectrometry. Positioned in a fixed-wing aircraft in a downward-looking mode, the spectrometer is used to detect ground sources of ethanol vapor from an altitude of 2000-3000 ft. Challenges to the use of this approach for the routine detection of chemical plumes include (1) the presence of a constantly changing background radiance as the aircraft flies, (2) the cost and complexity of collecting the data needed to train the classification algorithms used in implementing the plume detection, and (3) the need for rapid interferogram scans to minimize the ground area viewed per scan. To address these challenges, this work couples a novel ground-based data collection and training protocol with the use of signal processing and pattern recognition methods based on short sections of the interferogram data collected by the spectrometer. In the data collection, heated plumes of ethanol vapor are released from a portable emission stack and viewed by the spectrometer from ground level against a synthetic background designed to simulate a terrestrial radiance source. Classifiers trained with these data are subsequently tested with airborne data collected over a period of 2.5 years. Two classifier architectures are compared in this work: support vector machines (SVM) and piecewise linear discriminant analysis (PLDA). When applied to the airborne test data, the SVM classifiers perform best, failing to detect ethanol in only 8% of the cases in which it is present. False detections occur at a rate of less than 0.5%. The classifier performs well in spite of differences between the backgrounds associated with the ground-based and airborne data collections and the instrumental drift arising from the long time span of the data collection. Further improvements in classification performance are judged to require increased sophistication in the ground-based data collection in order to provide a better match to the infrared backgrounds observed from the air.

Air Pollutants↗
PubMed2005

An automated approach to connectivity-based partitioning of brain structures.

We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different areas of the brain, allowing them to interact with each other. Diffusion-tensor MRI measures the orientation of white-matter fibres in vivo, allowing us to perform connectivity-based partitioning non-invasively. Our new approach leverages atlas-based segmentation to automate anatomical labeling of the cortex. White-matter connectivities are inferred using a probabilistic tractography algorithm that models crossing pathways explicitly. The method is demonstrated with the partitioning of the corpus callosum of eight healthy subjects.

Algorithms↗
PubMed2006

Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy.

Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification.

Artificial Intelligence↗
PubMed2006

Implicit meshes for surface reconstruction.

Deformable 3D models can be represented either as traditional explicit surfaces, such as triangulated meshes, or as implicit surfaces. Explicit surfaces are widely accepted because they are simple to deform and render, but fitting them involves minimizing a nondifferentiable distance function. By contrast, implicit surfaces allow fitting by minimizing a differentiable algebraic distance, but are harder to meaningfully deform and render. Here, we propose a method that combines the strength of both approaches. It relies on a technique that can turn a completely arbitrary triangulated mesh, such as one taken from the Web, into an implicit surface that closely approximates it and can deform in tandem with it. This allows both automated algorithms to take advantage of the attractive properties of implicit surfaces for fitting purposes and people to use standard deformation tools they feel comfortable for interaction and animation purposes. We demonstrate the applicability of our technique to modeling the human upper-body, including face, neck, shoulders, and ears, from noisy stereo and silhouette data.

Algorithms↗
PubMed2005

Segmentation and size measurement of polyps in CT colonography.

Virtual colonoscopy is a relatively new method for the detection of colonic polyps. Their size, which is measured from reformatted CT images, mainly determines diagnosis. We present an automatic method for measuring the polyp size. The method is based on a robust segmentation method that grows a surface patch over the entire polyp surface starting from a seed. Projection of the patch points along the polyp axis yields a 2D point set to which we fit an ellipse. The long axis of the ellipse denotes the size of the polyp. We evaluate our method by comparing the automated size measurement with those of two radiologists using scans of a colon phantom. We give data for inter-observer and intra-observer variability of radiologists and our method as well as the accuracy and precision.

Algorithms↗
PubMed2003

Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio.

Neural spike sorting is an indispensable step in the analysis of multiunit extracellular neural signal recording. The applicability of spike sorting systems has been limited, mainly to the recording of sufficiently high signal-to-noise ratios, or to the cases where supervised classification can be utilized. We present a novel unsupervised method that shows satisfactory performance even under high background noise. The system consists of an efficient spike detector, a feature extractor that utilizes projection pursuit based on negentropy maximization (Huber, 1985 and Hyvarinen et al, 1999), and an unsupervised classifier based on probability density modeling using mixture of Gaussians (Jain et al., 2000). Our classifier is based on the mixture model with a roughly approximated number of Gaussians and subsequent mode-seeking. It does not require accurate estimation of the number of units present in the recording and, thus, is better suited for use in fully automated systems. The feature extraction stage leads to better performance than those utilizing principal component analysis and two nonlinear mappings for the recordings from the somatosensory cortex of rat and the abdominal ganglion of Aplysia. The classification method yielded correct classification ratio as high as 95%, for data where it was only 66% when a kappa-means-type algorithm was used for the classification stage.

Action Potentials↗
PubMed2004

Sequence patterns derived from the automated prediction of functional residues in structurally-aligned homologous protein families.

MOTIVATION: Most proteins have evolved to perform specific functions that are dependent on the adoption of well-defined three-dimensional (3D) structures. Specific patterns of conserved residues in amino acid sequences of divergently evolved proteins are frequently observed; these may reflect evolutionary restraints arising both from the need to maintain tertiary structure and the requirement to conserve residues more directly involved in function. Databases of such sequence patterns are valuable in identifying distant homologues, in predicting function and in the study of evolution. RESULTS: A fully automated database of protein sequence patterns, Functional Protein Sequence Pattern Database (FPSPD), has been derived from the analysis of the conserved residues that are predicted to be functional in structurally aligned homologous families in the HOMSTRAD database. Environment-dependent substitution tables, evolutionary trace analysis, solvent accessibility calculations and 3D-structures were used to obtain the FPSPD. The method yielded 3584 patterns that are considered functional and 3049 patterns that are probably functional. FPSPD could be useful for assigning a protein to a homologous superfamily and thereby providing clues about function. AVAILABILITY: FPSPD is available at http://www-cryst.bioc.cam.ac.uk/~fpspd/

Algorithms↗
PubMed2006

Automatic adaptive enhancement for images obtained with fiberscopic endoscopes.

Modern techniques for medical diagnostics and therapy in keyhole surgery scenarios as well as technical inspection make use of flexible endoscopes. Their characteristic bendable image conductor consists of a very limited number of coated fibers, which leads to so-called comb structure. This effect has a negative impact on further image processing steps such as feature tracking because these overlaid image structures are wrongly detected as image features. With respect to these tasks, we propose an automatic approach to generate optimal spectral filter masks for enhancement of fiberscopic images. We apply the Nyquist-Shannon sampling theorem to the spectrum of fiberscopically acquired images to obtain parameters for optimal filter mask calculation. This can be done automatically and independently of scale and resolution of the image conductor as well as type and resolution of the image sensor. We designed and verified simple rotation invariant masks as well as star-shaped rotation variant masks that contain information about orientation between the fiberscope and sensor. A subjective survey among experts between different modes of filtering certified the best results to the adapted star-shaped mask for high-quality glass fiberscopes. We furthermore define an objective metric to evaluate the results of different filter approaches, which verifies the results of the subjective survey. The proposed approach enables the automated reduction of fiberscopic comb structure. It is adaptive to arbitrary endoscope and sensor combinations. The results give the prospect of a large field of possible applications to reduce fiberscopic structure both for visual optimization in clinical environments and for further digital imaging tasks.

Algorithms↗
PubMed2006

Exact-mass library for pesticides using a molecular-feature database.

An automated molecular-feature database (MFD) consisting of the exact monoisotopic mass of 100 compounds, at least one exact mass product ion for each compound, and chromatographic retention time were used to identify pesticides in food and water samples. The MFD software compiles a list of accurate mass ions, excludes noise, and compares them with the monoisotopic exact masses in the database. The screening criteria consisted of +/-5 ppm accurate mass window, +/-0.2 min retention time window, and a minimum 1000 counts (signal-to-noise (S/N) ratio of approximately 10:1). The limit of detection for 100 tested compounds varied from <0.01 mg/kg for 72% of the compounds to <0.1 mg/kg for 95% of the compounds. The MFD search was useful for rapid screening and identification of pesticides in food and water, as shown in actual samples. The combined use of accurate mass and chromatographic retention time eliminated false positives in the automated analysis. The major weakness of the MFD is matrix interferences and loss of mass accuracy. Strengths of the MFD include rapid screening of 100 compounds at sensitive levels compared with a manual approach and the ease of use of the library for any accurate mass spectrometer instrumentation capable of routine sub-5-ppm mass accuracy.

Algorithms↗
PubMed2005

An automated genotyping system for analysis of HIV-1 and other microbial sequences.

MOTIVATION: Genetic analysis of HIV-1 is important not only for vaccine development, but also to guide treatment strategies, track the emergence of new viral variants and ensure that diagnostic assays are contemporary and fully optimized. However, most genotyping methods are laborious and complex, and involve the use of multiple software applications. Here, we describe the development of an automated genotyping system that can be easily applied to HIV-1 and other rapidly evolving viral pathogens. RESULTS: The new REGA subtyping tool, developed using Java programming and PERL scripts, combines phylogenetic analyses with boot-scanning methods for the genetic subtyping of full-length and subgenomic fragments of HIV-1. When used to investigate the subtype of previously published reference datasets that were analysed using manual phylogenetic methods, the automated method correctly identified 97.5-100% of non-recombinant and circulating recombinant forms of HIV-1, including 108 full-length, 108 gag and 221 env sequences downloaded from the Los Alamos database.

Algorithms↗
PubMed2006

Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models.

We introduce a new approach for the localization of 3D anatomical point landmarks. This approach is based on 3D parametric intensity models which are directly fitted to 3D images. To efficiently model tip-like, saddle-like, and sphere-like anatomical structures we introduce analytic intensity models based on the Gaussian error function in conjunction with 3D rigid transformations as well as deformations. To select a suitable size of the region-of-interest (ROI) where model fitting is performed, we also propose a new scheme for automatic selection of an optimal 3D ROI size based on the dominant gradient direction. In addition, to achieve a higher level of automation we present an algorithm for automatic initialization of the model parameters. Our approach has been successfully applied to accurately localize anatomical landmarks in 3D synthetic data as well as 3D MR and 3D CT image data. We have also compared the experimental results with the results of a previously proposed 3D differential approach. It turns out that the new approach significantly improves the localization accuracy.

Algorithms↗
PubMed1997

Automated detection of the left ventricular region in magnetic resonance images by Fuzzy c-Means model.

A new method for automated detection of the Left Ventricular (LV) region in Magnetic Resonance Imaging is presented. This method is based on the Fuzzy c-Means (FCM) clustering algorithm. The FCM is applied to each static frame of the cardiac cycle to detect the LV region. Delineation of this region is essential in the quantitative analysis of the cardiac function. The effectiveness of the method is demonstrated by application to sequences of cardiac images.

Algorithms↗
PubMed2003

Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions.

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women. In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.

Algorithms↗
PubMed2006

Temporal subtraction in chest radiography: automated assessment of registration accuracy.

Radiologists routinely compare multiple chest radiographs acquired from the same patient over time to more completely understand changes in anatomy and pathology. While such comparisons are achieved conventionally through a side-by-side display of images, image registration techniques have been developed to combine information from two separate radiographic images through construction of a "temporal subtraction image." Although temporal subtraction images provide a powerful mechanism for the enhanced visualization of subtle change, errors in the clinical evaluation of these images may arise from misregistration artifacts that can mimic or obscure pathologic change. We have developed a computerized method for the automated assessment of registration accuracy as demonstrated in temporal subtraction images created from radiographic chest image pairs. The registration accuracy of 150 temporal subtraction images constructed from the computed radiography images of 72 patients was rated manually using a five-point scale ranging from "5-excellent" to "1-poor;" ratings of 3, 4, or 5 reflected clinically acceptable subtraction images, and ratings of 1 or 2 reflected clinically unacceptable images. Gray-level histogram-based features and texture measures are computed at multiple spatial scales within a "lung mask" region that encompasses both lungs in the temporal subtraction images. A subset of these features is merged through a linear discriminant classifier. With a leave-one-out-by-patient training/testing paradigm, the automated method attained an A(z) value of 0.92 in distinguishing between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. A second linear discriminant classifier yielded an A(z) value of 0.82 based on a feature subset selected from an independent database of digitized film images. These methods are expected to advance the clinical utility of temporal subtraction images for chest radiography.

Algorithms↗
PubMed2006

A functional-based segmentation of human body scans in arbitrary postures.

This paper presents a general framework that aims to address the task of segmenting three-dimensional (3-D) scan data representing the human form into subsets which correspond to functional human body parts. Such a task is challenging due to the articulated and deformable nature of the human body. A salient feature of this framework is that it is able to cope with various body postures and is in addition robust to noise, holes, irregular sampling and rigid transformations. Although whole human body scanners are now capable of routinely capturing the shape of the whole body in machine readable format, they have not yet realized their potential to provide automatic extraction of key body measurements. Automated production of anthropometric databases is a prerequisite to satisfying the needs of certain industrial sectors (e.g., the clothing industry). This implies that in order to extract specific measurements of interest, whole body 3-D scan data must be segmented by machine into subsets corresponding to functional human body parts. However, previously reported attempts at automating the segmentation process suffer from various limitations, such as being restricted to a standard specific posture and being vulnerable to scan data artifacts. Our human body segmentation algorithm advances the state of the art to overcome the above limitations and we present experimental results obtained using both real and synthetic data that confirm the validity, effectiveness, and robustness of our approach.

Algorithms↗
PubMed2002

Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks.

There is an increasing need to develop powerful techniques to improve biomedical pattern discovery and visualization. This paper presents an automated approach, based on hybrid self-adaptive neural networks, to pattern identification and visualization for biomolecular data. The methods are tested on two datasets: leukemia expression data and DNA splice-junction sequences. Several supervised and unsupervised models are implemented and compared. A comprehensive evaluation study of some of their intrinsic mechanisms is presented. The results suggest that these tools may be useful to support biological knowledge discovery based on advanced classification and visualization tasks.

Algorithms↗
PubMed2002

[Research on automated detection technology of standard 12 lead synchronous ECG signal].

The parts of system automatically detecting the standard 12-synchronous-lead ECG (electrocardiograms) signal based on personal computer is introduced in this paper. The object-oriented programming method is adopted based on Windows System. We put forward methods to analyze QRS complex wave, P wave, T wave and ST fragment. In this paper the techniques such as ECG signal preprocessing, cardio wave parameters detecting and wave pattern cognizing are discussed too. Furthermore we use wavelet transform technique to analyze the wave pattern and get sound effect.

Algorithms↗
PubMed2003

Automated 3-D PDM construction from segmented images using deformable models.

In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.

Algorithms↗