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PubMedDate not supplied

A binary segmentation approach for boxing ribosome particles in cryo EM micrographs.

Three-dimensional reconstruction of ribosome particles from electron micrographs requires selection of many single-particle images. Roughly 100,000 particles are required to achieve approximately 10 A resolution. Manual selection of particles, by visual observation of the micrographs on a computer screen, is recognized as a bottleneck in automated single-particle reconstruction. This paper describes an efficient approach for automated boxing of ribosome particles in micrographs. Use of a fast, anisotropic non-linear reaction-diffusion method to pre-process micrographs and rank-leveling to enhance the contrast between particles and the background, followed by binary and morphological segmentation constitute the core of this technique. Modifying the shape of the particles to facilitate segmentation of individual particles within clusters and boxing the isolated particles is successfully attempted. Tests on a limited number of micrographs have shown that over 80% success is achieved in automatic particle picking.

Algorithms↗
PubMed2006

Semi-automated segmentation and visualisation of outer bone cortex from medical images.

Good segmentation of the outer bone cortex from medical images is a prerequisite for applications in the field of finite element analysis, surgical planning environments and personalised, case dependent, bone reconstruction. However, current segmentation procedures are often unsatisfactory. This study presents an automated filter procedure to generate a set of adapted contours from which a surface mesh can be deduced directly. The degree of interaction is user determined. The bone contours are extracted from the patients CT data by quick grey value segmentation. An extended filter procedure then only retains contour information representing the outer cortex as more specific internal loops and shape irregularities are removed, tailoring the image for the above-mentioned applications. The developed medical image based design methodology to convert contour sets of multiple bone types, from tibia tumour to neurocranium, is reported and discussed.

Algorithms↗
PubMed2003

An objective method to measure cell survival by computer-assisted image processing of numeric images of Petri dishes.

This work establishes an objective method to measure cell clonogenic survival by computer-assisted image processing using images of cell cultures fixed and stained in Petri dishes. The procedure, developed by Samba Technologies, consists of acquiring Petri dish pictures with a desktop scanner and analysing them by computer, using algorithms based on the 'top hat' filter. The results from the automated count for the cell line SQ20B are compared with those found by two observers, before and after normalization of the counting. After normalization, the shape of the survival curves of the 'manual' counting of the Petri dishes shows a good correlation between both observers. The software enables the small visible differences in count between observers to be eliminated. The comparison between the absolute number of colonies shows an increased difference between the two manual scorings that can be as great as 67 colonies, whereas the difference between the two automated counts is never greater than 8 colonies. These results demonstrate that the 'manual' count is inter- and intraobserver variable, whereas the automatic count performs reproducible cell colony counts, thereby minimizing user-generated bias. The large amount of data produced also gives information about cell and colony characteristics. Thus, this computer-assisted method has considerably improved the reliability of our statistical results.

Algorithms↗
PubMed2006

Integrated state evaluation for the images of crystallization droplets utilizing linear and nonlinear classifiers.

In a usual crystallization process, the researchers evaluate the protein crystallization growth states based on visual impressions and repeatedly assign scores throughout the growth process. Although the development of crystallization robotic systems has generally realised the automation of the setup and storage of crystallization samples, evaluation of crystallization states has not yet been completely automated. The method presented here attempts to categorize individual crystallization droplet images into five classes using multiple classifiers. In particular, linear and nonlinear classifiers are utilized. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process using linear discriminant analysis (LDA) and support vector machine (SVM). The performance of this method has been evaluated by comparing the results obtained using the method with the results obtained by a human expert and the concordance rate was 84.4%.

Algorithms↗
PubMed2005

Visualization-based discovery and analysis of genomic aberrations in microarray data.

BACKGROUND: Chromosomal copy number changes (aneuploidies) play a key role in cancer progression and molecular evolution. These copy number changes can be studied using microarray-based comparative genomic hybridization (array CGH) or gene expression microarrays. However, accurate identification of amplified or deleted regions requires a combination of visual and computational analysis of these microarray data. RESULTS: We have developed ChARMView, a visualization and analysis system for guided discovery of chromosomal abnormalities from microarray data. Our system facilitates manual or automated discovery of aneuploidies through dynamic visualization and integrated statistical analysis. ChARMView can be used with array CGH and gene expression microarray data, and multiple experiments can be viewed and analyzed simultaneously. CONCLUSION: ChARMView is an effective and accurate visualization and analysis system for recognizing even small aneuploidies or subtle expression biases, identifying recurring aberrations in sets of experiments, and pinpointing functionally relevant copy number changes. ChARMView is freely available under the GNU GPL at http://function.princeton.edu/ChARMView.

Algorithms↗
PubMed2005

Tree pattern matching in phylogenetic trees: automatic search for orthologs or paralogs in homologous gene sequence databases.

MOTIVATION: Comparative sequence analysis is widely used to study genome function and evolution. This approach first requires the identification of homologous genes and then the interpretation of their homology relationships (orthology or paralogy). To provide help in this complex task, we developed three databases of homologous genes containing sequences, multiple alignments and phylogenetic trees: HOBACGEN, HOVERGEN and HOGENOM. In this paper, we present two new tools for automating the search for orthologs or paralogs in these databases. RESULTS: First, we have developed and implemented an algorithm to infer speciation and duplication events by comparison of gene and species trees (tree reconciliation). Second, we have developed a general method to search in our databases the gene families for which the tree topology matches a peculiar tree pattern. This algorithm of unordered tree pattern matching has been implemented in the FamFetch graphical interface. With the help of a graphical editor, the user can specify the topology of the tree pattern, and set constraints on its nodes and leaves. Then, this pattern is compared with all the phylogenetic trees of the database, to retrieve the families in which one or several occurrences of this pattern are found. By specifying ad hoc patterns, it is therefore possible to identify orthologs in our databases.

Algorithms↗
PubMed1979

An image analysis system for cervical cytology automation using nuclear DNA content.

An experimental computer/image analysis system has been used to investigate cytology automation techniques based on nuclear DNA measurement and morphological artefact rejector tests. The system automatically measures and normalizes the integrated optical density of cell nuclei in specially prepared cervical cytology specimens, and selects any objects with abnormally high values for further analysis. These are then analyzed by morphological and densitometric tests designed to eliminate false positive signals caused by non-nuclear artefacts. The coordinates of the remaining abnormal nuclei are recorded so that they can subsequently be relocated and examined by a cytotechnician. Preliminary results are given showing the measurement accuracy of the system and the performance of the artefact rejection tests.

Cell Nucleus↗
PubMed2003

Automated lung nodule classification following automated nodule detection on CT: a serial approach.

We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the "detection task" and the "classification task." Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation.

Adult↗
PubMed2004

Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins.

MOTIVATION: Identification of potentially allergenic proteins is needed for the safety assessment of genetically modified foods, certain pharmaceuticals and various other products on the consumer market. Current methods in bioinformatic allergology exploit common features among allergens for the detection of amino acid sequences of potentially allergenic proteins. Features for identification still unexplored include the motifs occurring commonly in allergens, but rarely in ordinary proteins. In this paper, we present an algorithm for the identification of such motifs with the purpose of biocomputational detection of amino acid sequences of potential allergens. RESULTS: Identification of allergen-representative peptides (ARPs) with low or no occurrence in proteins lacking allergenic properties is the essential component of our new method, designated DASARP (Detection based on Automated Selection of Allergen-Representative Peptide). This approach consistently outperforms the criterion based on identical peptide match for predicting allergenicity recommended by ILSI/IFBC and FAO/WHO and shows results comparable to the alignment-based criterion as outlined by FAO/WHO. AVAILABILITY: The detection software and the ARP set needed for the analysis of a query protein reported here are properties of the Swedish National Food Agency and are available upon request. The protein sequence sets used in this work are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp. Allergenicity assessment for specific protein sequences of interest is also possible via ulfh@slv.se

Algorithms↗
PubMed2006

Curation of complex, context-dependent immunological data.

BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org. DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.

Allergy and Immunology↗
PubMedDate not supplied

A small angle light scattering device for planar connective tissue microstructural analysis.

The planar fibrous connective tissues of the body are composed of a dense extracellular network of collagen and elastin fibers embedded in a ground matrix, and thus can be thought of as biocomposites. Thus, the quantification of fiber architecture is an important step in developing an understanding of the mechanics of planar tissues in health and disease. We have used small angle light scattering (SALS) to map the gross fiber orientation of several soft membrane connective tissues. However, the device and analysis methods used in these studies required extensive manual intervention and were unsuitable for large-scale fiber architectural mapping studies. We have developed an improved SALS device that allows for rapid data acquisition, automated high spatial resolution specimen positioning, and new analysis methods suitable for large-scale mapping studies. Extensive validation experiments revealed that the SALS device can accurately measure fiber orientation for up to a tissue thickness of at least 500 microns to an angular resolution of approximately 1 degree and a spatial resolution of +/-254 microns. To demonstrate the new device's capabilities, structural measurements from porcine aortic valve leaflets are presented. Results indicate that the new SALS device provides an accurate method for rapid quantification of the gross fiber structure of planar connective tissues.

Animals↗
PubMed2004

[Automated classification of celestial spectra based on support vector machines].

The main objective of an automatic recognition system of celestial objects via their spectra is to classify celestial spectra and estimate physical parameters automatically. This paper proposes a new automatic classification method based on support vector machines to separate non-active objects from active objects via their spectra. With low SNR and unknown red-shift value, it is difficult to extract true spectral lines, and as a result, active objects can not be determined by finding strong spectral lines and the spectral classification between non-active and active objects becomes difficult. The proposed method in this paper combines the principal component analysis with support vector machines, and can automatically recognize the spectra of active objects with unknown red-shift values from non-active objects. It finds its applicability in the automatic processing of voluminous observed data from large sky surveys in astronomy.

Algorithms↗
PubMed2006

Identifying important concepts from medical documents.

Automated medical concept recognition is important for medical informatics such as medical document retrieval and text mining research. In this paper, we present a software tool called keyphrase identification program (KIP) for identifying topical concepts from medical documents. KIP combines two functions: noun phrase extraction and keyphrase identification. The former automatically extracts noun phrases from medical literature as keyphrase candidates. The latter assigns weights to extracted noun phrases for a medical document based on how important they are to that document and how domain specific they are in the medical domain. The experimental results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying important medical conceptual terms. They both performed better than the systems they were compared to.

Algorithms↗
PubMed2003

Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels.

We describe an automated method to locate the optic nerve in images of the ocular fundus. Our method uses a novel algorithm we call fuzzy convergence to determine the origination of the blood vessel network. We evaluate our method using 31 images of healthy retinas and 50 images of diseased retinas, containing such diverse symptoms as tortuous vessels, choroidal neovascularization, and hemorrhages that completely obscure the actual nerve. On this difficult data set, our method achieved 89% correct detection. We also compare our method against three simpler methods, demonstrating the performance improvement. All our images and data are freely available for other researchers to use in evaluating related methods.

Algorithms↗
PubMed2004

Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours.

Recording rolling leukocyte velocities from intravital microscopic video imagery is a critical task in inflammation research and drug validation. Since manual tracking is excessively time consuming, an automated method is desired. This paper illustrates an active contour based automated tracking method, where we propose a novel external force to guide the active contour that takes the hemodynamic flow direction into account. The construction of the proposed force field, referred to as motion gradient vector flow (MGVF), is accomplished by minimizing an energy functional involving the motion direction, and the image gradient magnitude. The tracking experiments demonstrate that MGVF can be used to track both slow- and fast-rolling leukocytes, thus extending the capture range of previously designed cell tracking techniques.

Algorithms↗
PubMed2005

Automating genomic data mining via a sequence-based matrix format and associative rule set.

There is an enormous amount of information encoded in each genome--enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands.

Base Sequence↗
PubMed2005

A new algorithm for content-based region query in databases with medical images.

This article presents an original method of implementation of the color set back-projection algorithm that is one of the most efficient method of automated detection of color regions from an image. The detected regions are then used in the content-based region query. The query is realized on one or more regions, having into consideration the color feature. The efficiency of the method was studied by means of a number of experiments effectuated with the help of a software system realized for this purpose, on a collection of medical images collected with an endoscope. The new method for the implementation of the algorithm is compared with the traditional one not only from the point of view of the execution time, but also from the point of view of the retrieval process quality.

Algorithms↗
PubMed1997

Computerized analysis of daily life motor activity for ambulatory monitoring.

The primary goal of an ambulatory monitoring of motor activities (AMMA) system is to document the occurrence of random and spontaneous motor activities (e.g., sitting, lying, standing, walking, running, etc.) of the ambulatory subject in natural environmental circumstances. Much progress has been made in recording fidelity, reduction in energy requirement, fixation of the accelerometers, equipment size and weight, memory capacity and data acquisition. At present, our laboratory is interested in developing an automated off-line AMMA-signal analysis system. The system has to take care of activity (wave) detection, recognition of onsets and endpoints of the various activities (waves), and computation of a set of relevant clinical parameters (e.g., total walking time, number of times rising from a chair, etc.) from long-term recorded data. Two methods are currently being used for computerizing the off-line analysis system: using an artificial neural network and using a set of selected features extracted from the input data. The present paper is aimed at the latter method. The method was successfully applied to long-term recorded data sets of eight male amputees and three other subjects. The primary results indicate that the method is a potentially useful too to computerize the off-line analysis system.

Activities of Daily Living↗