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PubMed1999

PRINTS prepares for the new millennium.

PRINTS is a diagnostic collection of protein fingerprints. Fingerprints exploit groups of motifs to build characteristic family signatures, offering improved diagnostic reliability over single-motif approaches by virtue of the mutual context provided by motif neighbours. Around 1000 fingerprints have now been created and stored in PRINTS. The September 1998 release (version 20.0), encodes approximately 5700 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. The database is accessible via the DbBrowser Web Server at http://www.biochem.ucl.ac.uk/bsm/dbbrowser /. In addition to supporting its continued growth, recent enhancements to the resource include a BLAST server, and more efficient fingerprint search software, with improved statistics for estimating the reliability of retrieved matches. Current efforts are focused on the design of more automated methods for database maintenance; implementation of an object-relational schema for efficient data management; and integration with PROSITE, profiles, Pfam and ProDom, as part of the international InterPro project, which aims to unify protein pattern databases and offer improved tools for genome analysis.

Amino Acid Sequence↗
PubMed2006

Automated left ventricular segmentation in cardiac MRI.

We present an automated left ventricular (LV) myocardial boundary extraction method. Automatic localization of the LV is achieved using a motion map and an expectation maximization algorithm. The myocardial region is then segmented using an intensity-based fuzzy affinity map and the myocardial contours are extracted by cost minimization through a dynamic programming approach. The results from the automated algorithm compared against the experienced radiologists using Bland and Altman analysis were found to have consistent mean bias of 7% and limits of agreement comparable to the inter-observer variability inherent in the manual method.

Adult↗
PubMed1979

Rapid analysis of hematology image data: the ADC-500 preprocessor.

A sequential, pipeline processor (that we have named the ADC-500 preprocessor) has been developed which scene segments the three color image data from the ADC-500 optics one image element at a time, groups together image elements from each object in the scene and extracts features from each object. The processing occurs at television frame rates, requiring 16.7 msec to process the entire image. This speed was instrumental in allowing the ADC-500 automated differential analyzer to perform routine 500-cell differentials. The preprocessor also contains hardware which simplifies compilation of the three color histograms. The segmentation algorithms implemented in the preprocessor are multicolor extensions of the classical monochrome density histogram threshold method. For most cell image analysis tasks, a sequential pipeline processor of this type should be more economical and as fast or faster than a parallel processor.

Blood Cells↗
PubMed2006

Protein classification using ontology classification.

MOTIVATION: The classification of proteins expressed by an organism is an important step in understanding the molecular biology of that organism. Traditionally, this classification has been performed by human experts. Human knowledge can recognise the functional properties that are sufficient to place an individual gene product into a particular protein family group. Automation of this task usually fails to meet the 'gold standard' of the human annotator because of the difficult recognition stage. The growing number of genomes, the rapid changes in knowledge and the central role of classification in the annotation process, however, motivates the need to automate this process. RESULTS: We capture human understanding of how to recognise members of the protein phosphatases family by domain architecture as an ontology. By describing protein instances in terms of the domains they contain, it is possible to use description logic reasoners and our ontology to assign those proteins to a protein family class. We have tested our system on classifying the protein phosphatases of the human and Aspergillus fumigatus genomes and found that our knowledge-based, automatic classification matches, and sometimes surpasses, that of the human annotators. We have made the classification process fast and reproducible and, where appropriate knowledge is available, the method can potentially be generalised for use with any protein family. AVAILABILITY: All components described in this paper are freely available. OWL ontology http://www.bioinf.man.ac.uk/phosphabase myGrid http://www.mygrid.org.uk Instance Store http://instancestore.man.ac.uk.

Algorithms↗
PubMed2006

CT angiography of peripheral arterial bypass grafts: Accuracy and time-effectiveness of quantitative image analysis with an automated software tool.

RATIONALE AND OBJECTIVES: Qualitative analysis of computed tomography (CT) angiography data often is limited by intra- and interobserver variability. The purpose of this study was to evaluate the time-effectiveness and accuracy of a quantitative CT angiography data analysis using automated software in comparison with qualitative axial and coronal CT image reading in patients with peripheral bypass grafts. MATERIALS AND METHODS: Twenty-eight patients with 33 saphenous bypass grafts underwent 4-channel (n = 21) and 16-channel (n = 7) CT angiography. Two readers evaluated in consensus the CT data qualitatively on axial and coronal reconstructions and with the software regarding the presence of graft stenoses, aneurysmal changes, and arteriovenous fistulas. The time for data analysis was taken and the accuracy was compared with the results from digital subtraction angiography (DSA). RESULTS: No significant difference was present between data analysis time using axial and coronal CT images (4.9 +/- 1.5 minutes) and when using the software tool (5.5 +/- 1.4 minutes). Good (kappa = 0.652) to excellent (kappa = 1.000) intermodality agreement was present between qualitative and quantitative CT analysis regarding graft-related abnormalities. Sensitivity and specificity for diagnosing stenoses, aneurysms, and fistula did not differ significantly (P > .025) between qualitative CT image reading and the automated software tool. CONCLUSIONS: CT angiography analysis of peripheral bypass grafts using an automated software tool is similar regarding time-effectiveness and accuracy when compared with qualitative CT data analysis on axial and coronal images. It may assist in determining the significance of an abnormality and can yield objective morphometric data of vessel calibers.

Algorithms↗
PubMed2006

Automated identification of adverse events related to central venous catheters.

Methods for surveillance of adverse events (AEs) in clinical settings are limited by cost, technology, and appropriate data availability. In this study, two methods for semi-automated review of text records within the Veterans Administration database are utilized to identify AEs related to the placement of central venous catheters (CVCs): a Natural Language Processing program and a phrase-matching algorithm. A sample of manually reviewed records were then compared to the results of both methods to assess sensitivity and specificity. The phrase-matching algorithm was found to be a sensitive but relatively non-specific method, whereas a natural language processing system was significantly more specific but less sensitive. Positive predictive values for each method estimated the CVC-associated AE rate at this institution to be 6.4 and 6.2%, respectively. Using both methods together results in acceptable sensitivity and specificity (72.0 and 80.1%, respectively). All methods including manual chart review are limited by incomplete or inaccurate clinician documentation. A secondary finding was related to the completeness of administrative data (ICD-9 and CPT codes) used to identify intensive care unit patients in whom a CVC was placed. Administrative data identified less than 11% of patients who had a CVC placed. This suggests that other methods, including automated methods such as phrase matching, may be more sensitive than administrative data in identifying patients with devices. Considerable potential exists for the use of such methods for the identification of patients at risk, AE surveillance, and prevention of AEs through decision support technologies.

Artificial Intelligence↗
PubMed2005

An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms.

Electron tomography allows for the determination of the three-dimensional structures of cells and tissues at resolutions significantly higher than that which is possible with optical microscopy. Electron tomograms contain, in principle, vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles. The development of reliable quantitative approaches for the analysis of features in tomograms is an important problem, and a challenging prospect due to the low signal-to-noise ratios that are inherent to biological electron microscopic images. This is, in part, a consequence of the tremendous complexity of biological specimens. We report on a new method for the automated segmentation of HIV particles and selected cellular compartments in electron tomograms recorded from fixed, plastic-embedded sections derived from HIV-infected human macrophages. Individual features in the tomogram are segmented using a novel robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features. The optimization is carried out in a transformed spherical domain with the center an interior point of the particle of interest, providing a proper setting for the fast and accurate minimization of the segmentation energy. This method provides tools for the semi-automated detection and statistical evaluation of HIV particles at different stages of assembly in the cells and presents opportunities for correlation with biochemical markers of HIV infection. The segmentation algorithm developed here forms the basis of the automated analysis of electron tomograms and will be especially useful given the rapid increases in the rate of data acquisition. It could also enable studies of much larger data sets, such as those which might be obtained from the tomographic analysis of HIV-infected cells from studies of large populations.

Algorithms↗
PubMed2003

A novel approach for high-quality microarray processing using third-dye array visualization technology.

Historically, microarray image processing has been technically challenging in obtaining quality gene expression data. After hybridization of Cy3- and Cy5-labeled samples, images are collected and processed to obtain gene expression ratio measurements for each of the elements on the array. The hybridization process often brings in contaminating noise, which can make correct identification of the signal difficult. In addition, spot intensity levels are highly variable due to the expression differences of different genes, and weak spots are often difficult to detect. These conditions are further complicated by inherent irregularities in spot position, shape, and size commonly found on high-density microarrays, making image processing an often labor-intensive task that is difficult to reliably automate. We previously reported a novel third-dye array visualization (TDAV) technology that allows prehybridization visualization and quality control of printed arrays. Here, we present a new microarray image processing approach utilizing TDAV. By incorporating the third-dye image, we show that overall quality of the microarray data is significantly improved, and automation of processing is feasible and reliable. Furthermore, we demonstrate use of the third-dye image to better quality control microarray image analysis. Both the principle and implementation of the approach are presented in detail, with experimental results.

Algorithms↗
PubMed2004

Gene clustering by latent semantic indexing of MEDLINE abstracts.

MOTIVATION: A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations. RESULTS: We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments. AVAILABILITY: The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html.

Abstracting and Indexing↗
PubMed2004

Computerized characterization of breast masses on three-dimensional ultrasound volumes.

We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

Algorithms↗
PubMed1999

Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

The purpose of this study was to develop and test a computerized method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method was used to identify the aerated lung regions in 600 chest radiographs. Minimal a priori lung morphology information was required for this gray-level thresholding-based segmentation. Consequently, segmentation was applicable to grossly abnormal cases. The relative areas of segmented right and left lung regions in each image were compared with the corresponding area distributions of normal images to determine the presence of abnormal asymmetry. Computerized diagnoses were compared with image ratings assigned by a radiologist. The ability of the automated method to distinguish normal from asymmetrically abnormal cases was evaluated by using receiver operating characteristic (ROC) analysis, which yielded an area under the ROC curve of 0.84. This automated method demonstrated promising performance in its ability to detect abnormal asymmetry in PA chest images. We believe this method could play a role in a picture archiving and communications (PACS) environment to immediately identify abnormal cases and to function as one component of a multifaceted computer-aided diagnostic scheme.

Databases as Topic↗
PubMed2005

Quantitative characterization of airspace enlargement in emphysema.

The mean linear intercept (L(m)) can be used to estimate the surface area for gas exchange in the lung. However, in recent years, it is most commonly used as an index for characterizing the enlargement of airspaces in emphysema and the associated severity of structural destruction in the lung. Specifically, an increase in L(m) is thought to result from an increase in airspace sizes. In this paper, we examined how accurately L(m) measures the linear dimensions of airspaces from histological sections and a variety of computer-generated test images. To this end, we developed an automated method for measuring linear intercepts from digitized images of tissue sections and calculate L(m) as their mean. We examined how the shape of airspaces and the variability of their sizes influence L(m) as well as the distribution of linear intercepts. We found that, for a relatively homogeneous enlargement of airspaces, L(m) was a reliable index for detecting emphysema. However, in the presence of spatial heterogeneities with a large variability of airspace sizes, L(m) did not significantly increase and sometimes even decreased compared with its value in normal tissue. We also developed an automated method for measuring the area and computed an equivalent diameter of each individual airspace that is independent of shape. Finally, we introduced new indexes based on the moments of diameter that we found to be more reliable than L(m) to characterize airspace enlargement in the presence of heterogeneities.

Algorithms↗
PubMed1999

EICO-1: an orthodontist-maintained expert system in clinical orthodontics.

Expert systems are increasingly being used to provide comprehensive interpretative services for diagnosis and treatment planning. Some of these systems are constrained by the complexities of rule-based strategies and a need for knowledge engineers throughout the maintenance phase. A new approach to knowledge acquisition known as Ripple-Down-Rules was used in the development of EICO-1 (Expert Interpretation in Clinical Orthodontics). This expert-maintained system for automating orthodontic reports has a knowledge base of six hundred and eighty rules, and is maintained by an expert trained only in Orthodontics and without the help of knowledge engineers. EICO-1 is the first expert system in Dentistry to use Ripple-Down-Rules. It has potential as an interactive advisory tool and is applicable in a clinical situation.

Artificial Intelligence↗
PubMed2006

Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer's disease.

We describe a new algorithm for the automated segmentation of the hippocampus (Hc) and the amygdala (Am) in clinical Magnetic Resonance Imaging (MRI) scans. Based on homotopically deforming regions, our iterative approach allows the simultaneous extraction of both structures, by means of dual competitive growth. One of the most original features of our approach is the deformation constraint based on prior knowledge of anatomical features that are automatically retrieved from the MRI data. The only manual intervention consists of the definition of a bounding box and positioning of two seeds; total execution time for the two structures is between 5 and 7 min including initialisation. The method is evaluated on 16 young healthy subjects and 8 patients with Alzheimer's disease (AD) for whom the atrophy ranged from limited to severe. Three aspects of the performances are characterised for validating the method: accuracy (automated vs. manual segmentations), reproducibility of the automated segmentation and reproducibility of the manual segmentation. For 16 young healthy subjects, accuracy is characterised by mean relative volume error/overlap/maximal boundary distance of 7%/84%/4.5 mm for Hc and 12%/81%/3.9 mm for Am; for 8 Alzheimer's disease patients, it is 9%/84%/6.5 mm for Hc and 15%/76%/4.5 mm for Am. We conclude that the performance of this new approach in data from healthy and diseased subjects in terms of segmentation quality, reproducibility and time efficiency compares favourably with that of previously published manual and automated segmentation methods. The proposed approach provides a new framework for further developments in quantitative analyses of the pathological hippocampus and amygdala in MRI scans.

Adult↗
PubMed2005

Clutter invariant ATR.

One of the central problems in Automated Target Recognition is to accommodate the infinite variety of clutter in real military environments. The principle focus of our paper is on the construction of metric spaces where the metric measures the distance between objects of interest invariant to the infinite variety of clutter. Such metrics are formulated using second-order random field models. Our results indicate that this approach significantly improves detection/classification rates of targets in clutter.

Algorithms↗
PubMedDate not supplied

Automated computer screening of chest radiographs for pneumoconiosis.

The results of two complementary approaches for performing diagnostic screening for the presence of coal workers' pneumoconiosis (CWP) from the routine posterior-anterior chest radiograph are presented. The first is a digital approach utilizing the measurement of image texture, while the second uses hybrid optical-digital methods involving the optical Fourier transform. Both approaches yield classification results comparable to experienced radiologists.

Coal Mining↗
PubMed1998

Neural networks for recognizing patterns in cardiotocograms.

The cardiotocogram (CTG) is commonly used for routine fetal monitoring in the delivery room. A major problem is that the interpretation of the CTG trace requires experienced specialists. In order to avoid long gaps between the detection of a suspicious pattern and the intervention, the CTG has to be checked in short intervals. An automated monitoring system at the obstetric site can reduce such delays. Therefore, an alarm system immediately reporting suspicious events has been built. The focus of our study was put on the question whether AI techniques such as neural networks are suited to the task of recognizing patterns in the CTG trace. In a comparative study, their performance was evaluated against that of conventional methods. The neural networks turned out to provide significantly better results than the tested conventional methods.

Cardiotocography↗
PubMed2007

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data.

BACKGROUND: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process. RESULTS: The clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License. CONCLUSION: The FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis.

Algorithms↗