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PubMed1992

Diagnosing periapical bone lesions on radiographs by means of texture analysis.

Trabecular pattern, the radiographic projection of trabecular bone, is a repeated structure that appears in a dental radiograph. Texture analysis, the computer image analysis of repeated patterns, is a technique that can be used to automate the diagnosis of periapical lesions with the detection of the absence of the texture that corresponds to the trabecular bone. The purpose of this study was to determine whether it is feasible to use texture analysis to identify the presence of the trabecular pattern in radiographs and to detect a periapical bone lesion based on a local absence of this pattern. Thirty-two mandibular periapical films, 16 with and 16 without periapical lesions, were used in this study. Texture analysis was carried out on the digital images of these radiographs. In the 16 films with lesions, they were all correctly identified, and no lesions were found in the 16 films without lesions. This result is based on the a prior knowledge of the user about the localization of the disease. Locating periapical regions without user interaction is a goal for future research.

Alveolar Process↗
PubMed2002

Computer-aided detection and diagnosis at the start of the third millennium.

Computer-aided diagnosis has been under development for more than 3 decades. The rate of progress appears exponential, with either recent approval or pending approval for devices focusing on mammography, chest radiographs, and chest CT. Related technologies improve diagnosis for many other types of medical images including virtual colonography, vascular imaging, as well as automated quantitation of image-derived metrics. A variety of techniques are currently employed with success, likely reflecting the variety of imagery used, as well as the variety of tasks. Most areas of medical imaging have had efforts at computer assistance, and some have even received FDA approval and can be reimbursed. We anticipate that the rapid advance of these technologies will continue, and that application will broaden to cover much of medical imaging. Acceptance of, and integration of computer-aided diagnosis technology with the electronic radiology practice is a current challenge. These challenges will be overcome, and we expect that computer-aided diagnosis will be routinely applied to medical images.

Colonography, Computed Tomographic↗
PubMed2002

ESyPred3D: Prediction of proteins 3D structures.

MOTIVATION: Homology or comparative modeling is currently the most accurate method to predict the three-dimensional structure of proteins. It generally consists in four steps: (1) databanks searching to identify the structural homolog, (2) target-template alignment, (3) model building and optimization, and (4) model evaluation. The target-template alignment step is generally accepted as the most critical step in homology modeling. RESULTS: We present here ESyPred3D, a new automated homology modeling program. The method gets benefit of the increased alignment performances of a new alignment strategy. Alignments are obtained by combining, weighting and screening the results of several multiple alignment programs. The final three-dimensional structure is build using the modeling package MODELLER. ESyPred3D was tested on 13 targets in the CASP4 experiment (Critical Assessment of Techniques for Proteins Structural Prediction). Our alignment strategy obtains better results compared to PSI-BLAST alignments and ESyPred3D alignments are among the most accurate compared to those of participants having used the same template. AVAILABILITY: ESyPred3D is available through its web site at http://www.fundp.ac.be/urbm/bioinfo/esypred/ CONTACT: christophe.lambert@fundp.ac.be; http://www.fundp.ac.be/~lambertc

Algorithms↗
PubMed2005

Automated variable weighting in k-means type clustering.

This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.

Algorithms↗
PubMed2006

Analog seizure detection and performance evaluation.

Epilepsy is the most prevalent neurological disorder affecting both adults and children. Over two-and-one-half million individuals in the United States have epilepsy and 25% of them do not respond to drugs. A significant focus of current research efforts is the development of a fully implantable device for real-time seizure detection and automated warning and blockage of seizures. The purpose of this paper is to describe and demonstrate the feasibility of incorporating a novel tool, the percentile tracking filter into a successful, validated seizure detection algorithm to create an analog seizure detection device. We demonstrate, in a small-scale study, that the performance of this analog implementation is statistically similar to a digital implementation of a previously described and successfully validated seizure digital algorithm. This analog implementation can be realized into an application specific integrated circuit that is suitable for a fully implantable device for seizure monitoring, warning and treatment, which is likely to consume very little power, a feature of practical value.

Algorithms↗
PubMed2005

A simple approach for protein name identification: prospects and limits.

BACKGROUND: Significant parts of biological knowledge are available only as unstructured text in articles of biomedical journals. By automatically identifying gene and gene product (protein) names and mapping these to unique database identifiers, it becomes possible to extract and integrate information from articles and various data sources. We present a simple and efficient approach that identifies gene and protein names in texts and returns database identifiers for matches. It has been evaluated in the recent BioCreAtIvE entity extraction and mention normalization task by an independent jury. METHODS: Our approach is based on the use of synonym lists that map the unique database identifiers for each gene/protein to the different synonym names. For yeast and mouse, synonym lists were used as provided by the organizers who generated them from public model organism databases. The synonym list for fly was generated directly from the corresponding organism database. The lists were then extensively curated in largely automated procedure and matched against MEDLINE abstracts by exact text matching. Rule-based and support vector machine-based post filters were designed and applied to improve precision. RESULTS: Our procedure showed high recall and precision with F-measures of 0.897 for yeast and 0.764/0.773 for mouse in the BioCreAtIvE assessment (Task 1B) and 0.768 for fly in a post-evaluation. CONCLUSION: The results were close to the best over all submissions. Depending on the synonym properties it can be crucial to consider context and to filter out erroneous matches. This is especially important for fly, which has a very challenging nomenclature for the protein name identification task. Here, the support vector machine-based post filter proved to be very effective.

Animals↗
PubMedDate not supplied

Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information within an expert system.

The authors are developing an expert-system electrocardiogram (ECG) arrhythmia detector (HOBBES) for automated, long-term rhythm analysis. HOBBES employs rules and procedures that emulate how human experts analyze ECGs. This paper describes methods that HOBBES employs for improving error detection and correction in processing noisy ECGs. During periods of clean data, HOBBES develops a knowledge base that describes typical beat shapes, typical interbeat intervals between beats of different types, and patterns of beat sequences that it has observed. During periods of noisy data, HOBBES applies the information learned from the clean data to reject artifact and classify beats. HOBBES was evaluated in a noise-stress test using 35 half-hour ECG records containing a mixture of supraventricular and ventricular ectopy in normal sinus rhythm. In comparison with a classical arrhythmia detector (ARISTOTLE), HOBBES increased the number of correctly classified beats and enhanced the rejection of artifact.

Arrhythmias, Cardiac↗
PubMed1993

A knowledge based interpretation system for EMG abnormalities.

The conventional method of diagnosis in electromyography is complex and time consuming, not only due to the large number of parameters, to be considered for diagnosis, but also because of the usual procedure of evaluating the different parameters of EMG signal by visual scanning of the plotted signal. So there is a clear need to make use of computer aided decision support system. In the present work an attempt has been made in the direction of integration into one automated system, the qualitative knowledge of the physician, with possibly sophisticated signal analysis tools which must replace the visual scanning. A software program (in Turbo-C) on a PC-AT has been developed to evaluate the different parameters of MUAP's (motor unit action potential) in a EMG signal. Then an Expert system (in Turbo-Prolog) has been implemented for diagnostic purposes of different muscular abnormalities by making a knowledge base from the different parameters involved in the decision making procedure of clinical electromyography. A hybrid model of rule and frame based Expert system is implemented. An attempt has been made for making a complete system, i.e., for recording, analysis and decision making for diagnosis.

Diagnosis, Computer-Assisted↗
PubMed2005

Gene/protein name recognition based on support vector machine using dictionary as features.

BACKGROUND: Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. RESULTS: In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. CONCLUSION: During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Algorithms↗
PubMed2005

Augmented cell-graphs for automated cancer diagnosis.

This work reports a novel computational method based on augmented cell-graphs (ACG), which are constructed from low-magnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both the nodes and the edges of an ACG are assigned weights to capture more information about the topology of the tissue. In this work, the experiments are conducted on a dataset that is comprised of 646 human brain biopsy samples from 60 different patients. It is shown that the ACG approach yields sensitivity of 97.53% and specificities of 93.33 and 98.15% (for the inflamed and healthy, respectively) at the tissue level in glioma diagnosis.

Algorithms↗
PubMed2000

Systematic and fully automated identification of protein sequence patterns.

We present an efficient algorithm to systematically and automatically identify patterns in protein sequence families. The procedure is based on the Splash deterministic pattern discovery algorithm and on a framework to assess the statistical significance of patterns. We demonstrate its application to the fully automated discovery of patterns in 974 PROSITE families (the complete subset of PROSITE families which are defined by patterns and contain DR records). Splash generates patterns with better specificity and undiminished sensitivity, or vice versa, in 28% of the families; identical statistics were obtained in 48% of the families, worse statistics in 15%, and mixed behavior in the remaining 9%. In about 75% of the cases, Splash patterns identify sequence sites that overlap more than 50% with the corresponding PROSITE pattern. The procedure is sufficiently rapid to enable its use for daily curation of existing motif and profile databases. Third, our results show that the statistical significance of discovered patterns correlates well with their biological significance. The trypsin subfamily of serine proteases is used to illustrate this method's ability to exhaustively discover all motifs in a family that are statistically and biologically significant. Finally, we discuss applications of sequence patterns to multiple sequence alignment and the training of more sensitive score-based motif models, akin to the procedure used by PSI-BLAST. All results are available at httpl//www.research.ibm.com/spat/.

Algorithms↗
PubMed2006

An automated procedure for the extraction of metabolic network information from time series data.

Novel high-throughput measurement techniques in vivo are beginning to produce dense high-quality time series which can be used to investigate the structure and regulation of biochemical networks. We propose an automated information extraction procedure which takes advantage of the unique S-system structure and supports model building from time traces, curve fitting, model selection, and structure identification based on parameter estimation. The procedure comprises of three modules: model Generation, parameter estimation or model Fitting, and model Selection (GFS algorithm). The GFS algorithm has been implemented in MATLAB and returns a list of candidate S-systems which adequately explain the data and guides the search to the most plausible model for the time series under study. By combining two strategies (namely decoupling and limiting connectivity) with methods of data smoothing, the proposed algorithm is scalable up to realistic situations of moderate size. We illustrate the proposed methodology with a didactic example.

Algorithms↗
PubMed2000

Unstable Morse code recognition with adaptive variable-ratio threshold prediction for physically disabled persons.

With one or two switches, Morse code could provide an effective alternative communication channel for individuals with physical limitations. However, most of the physically disabled persons have difficulties in maintaining a stable typing of Morse code, and hence the automated recognition of unstable Morse code is becoming more on demand. In this study, an adaptive variable-ratio threshold prediction (AVRTP) algorithm is proposed to analyze the Morse code time series with variable unit time period and ratio. Two least-mean-square (LMS) predictors are applied to track the dot interval and the dot-dash difference concurrently, and then a predicted threshold based on a variable-ratio decision rule is used to distinguish between dots and dashes. The same method is also applied to identify character-spaces. By the adaptive prediction of variable-ratio threshold, AVRTP has successfully overcome the difficulty of analyzing severely unstable Morse code time series and outperformed the previously proposed adaptive unstable-speed prediction (AUSP) algorithm and LMS and matching (I,MS&M) algorithm. This study concludes with a computer simulation and a preliminary clinical evaluation that demonstrate AVRTP as an efficient and reliable method for unstable Morse code recognition.

Adolescent↗
PubMed2006

On the adaptive detection of blood vessels in retinal images.

This paper proposes an automated blood vessel detection scheme based on adaptive contrast enhancement, feature extraction, and tracing. Feature extraction of small blood vessels is performed by using the standard deviation of Gabor filter responses. Tracing of vessels is done via forward detection, bifurcation identification, and backward verification. Tests over twenty images show that for normal images, the true positive rate (TPR) ranges from 80% to 91%, and their corresponding false positive rates (FPR) range from 2.8% to 5.5%. For abnormal images, the TPR ranges from 73.8% to 86.5% and the FPR ranges from 2.1% to 5.3%, respectively. In comparison with two published solution schemes that were also based on the STARE database, our scheme has lower FPR for the reported TPR measure.

Algorithms↗
PubMed2006

AMDA: an R package for the automated microarray data analysis.

BACKGROUND: Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. Microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users. RESULTS: To address these problems we have developed an automated microarray data analysis (AMDA) software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation. CONCLUSION: AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopolis http://www.genopolis.it/.

Algorithms↗
PubMed1997

Automated detection of hereditary syndromes using data mining.

Computer-based data mining methodology applied to family history clinical data can algorithmically create highly accurate, clinically oriented hereditary disease pattern recognizers. For the example of hereditary colon cancer, the data mining's selection of relevant factors to assess for hereditary colon cancer was statistically significant (P < 0.05). All final recognizer-formulated patterns of hereditary colon cancer were independently confirmed by a clinical expert. Applied to previously analyzed family histories, the recognizer identified the definitive hereditary histories, correctly responded negatively to the putative hereditary histories, and correctly responded negatively to empirically elevated colon cancer risk situations. This capability facilitates patient selection for DNA studies in search of gene mutations. When genetic mutations are included as parameters in a patient database for a genetic disease, the process yields an expert system which characterizes variations in clinical disease presentations in terms of genetic mutations. Such information can greatly improve the efficiency of gene testing.

Adult↗
PubMed2006

Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics.

Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein's subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases.

Animals↗
PubMed2003

A global energy function for the alignment of serially acquired slices.

An accurate, computationally efficient, and fully automated algorithm for the alignment of two-dimensional (2-D) serially acquired sections forming a three-dimensional (3-D) volume is presented. The approach relies on the optimization of a global energy function, based on the object shape, measuring the similarity between a slice and its neighborhood in the 3-D volume. Slice similarity is computed using the distance transform measure in both directions. No particular direction is privileged in the method avoiding global offsets, biases in the estimation and error propagation. The method was evaluated on real images [medical, biological, and other computerized tomography (CT) scanned 3-D data] and the experimental results demonstrated its accuracy as reconstuction errors are less than one degree in rotation and less than one pixel in translation.

Algorithms↗