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PubMed2003

Segmentation and 3D reconstruction of biological cells from serial slice images.

Our understanding of the world around us and the many objects that we encounter is based primarily on three-dimensional information. It is simply part of the environment in which we live and the intuitive nature of our interpretation of our surroundings. In the arena of biomedical imaging, the image information most often collected is in the form of two-dimensional images. In cases where serial slice information is obtained, such as MRI images, it is still difficult for the observer to mentally build and understand the three-dimensional structure of the object. Although most image rendering software packages allow for 3D views of the serial sections, they lack the ability to segment, or isolated different objects in the data set. Typically the task of segmentation is performed by knowledgeable persons who tediously outline or label the object of interest in each image slice containing the object [1,2]. It remains a difficult challenge to train a computer to understand an image and aid in this process of segmentation. This article reports of on-going work in developing a semi-automated segmentation technique. The approach uses a Leica Confocal Laser Scanning Microscope (CLSM) to collect serial slice images, image rendering and manipulating software called IMOD (Boulder Colorado), and Matlab (The Mathworks Inc.) image processing tools for development of the object segmentation routines. The initial objects are simple fluorescent microspheres (Molecular Probes), which are easily imaged and segmented. The second objects are rat enteric neurons, which provide medium complexity in shape and size. Finally, the work will be applied to the biological cells of the household .y, Musca domestica, to further understand how its vision system operates.

Algorithms↗

PubMed1981

TULIPS: the Uppsala-Linkoping Image Processing System.

The Uppsala-Linkoping Image Processing System, TULIPS, is described. TULIPS, a hardware-software system designed for cell image processing, was developed at Uppsala University Hospital in cooperation with the Department of Electrical Engineering at Linkoping University. The hardware part of the image processing system is built around a high-speed data bus with a capacity of about 40 M byte/sec connected to a PDP-11/55 host computer. An image memory, an LSI-11 microcomputer and a video interface for displaying the image memory content on a TV monitor are also connected to the high-speed bus. An automated microscope and a "Poulsen processor" for low resolution segmentation, both to be attached to the high-speed bus, are being developed. A monitor and an interpreter for an image processing language have been implemented on the host computer. This software system allows interactive, as well as batch, processing. The degree of user interaction is easily adapted to the user's needs. The image processing language is command oriented, and it is easily expanded by adding new commands. The system has been used both for studies in the field of quantitative microscopy and as a platform for development and testing of new image processing algorithms.

Computers↗

PubMed2002

Feature-based, automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging.

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.

Algorithms↗

PubMedDate not supplied

Automatic registration of multiple skin lesions by use of point pattern matching.

Computerized comparison of serial skin images is a potentially valuable tool for melanoma screening. In automating this process, matching or "registering" each lesion in a pair of images plays an important role in looking for clinically significant change. We have investigated three practical techniques--a point pattern correlation, a 2-point geometrical transformation, and a 3-point geometrical transformation--for their effectiveness in matching and identifying lesions in pairs of skin images. These techniques view the spots in each image as a point pattern to be matched from image to image. Each of these methods is shown to be quite effective as long as one or more known initial match points can be provided. Experiments performed by imaging actual patients under realistic conditions indicate that the 3-point transformation algorithm performs the best overall, achieving an average matching accuracy of 97%. The nature of these algorithms, their relative performance under a range of conditions, and possible methods for improving accuracies are discussed.

Algorithms↗

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↗

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↗

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↗

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↗

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↗

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↗

PubMed2003

A robust statistics-based global energy function for the alignment of serially acquired autoradiographic sections.

Autoradiographic analysis of the functional changes occurring in the rat brain are most often performed on coronal sections that allow a good insight into the events occurring at the structural level but lacks the 3D context which is necessary to fully understand the involvement of the brain structures in specific situations like focal seizures with or without generalization. Therefore a robust, fully-automated algorithm for the registration of serially acquired autoradiographic sections is presented. The method accounts for the main difficulties of autoradiographic alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices, non parallel or missing slices. The approach relies on the minimization of a global energy function based on robust statistics. The energy function measures the similarity between a slice and its neighborhood in the 3D volume. No particular direction is privileged in the method, so that global offsets, biases in the estimation or error propagations are avoided. The method is evaluated qualitatively and quantitatively on real autoradiographic data. Rat brain autoradiographic volumes are reconstructed with registration errors less than 1 degree in rotation and less than 1 pixel in translation.

Algorithms↗

PubMed2002

Inferring sub-cellular localization through automated lexical analysis.

MOTIVATION: The SWISS-PROT sequence database contains keywords of functional annotations for many proteins. In contrast, information about the sub-cellular localization is available for only a few proteins. Experts can often infer localization from keywords describing protein function. We developed LOCkey, a fully automated method for lexical analysis of SWISS-PROT keywords that assigns sub-cellular localization. With the rapid growth in sequence data, the biochemical characterisation of sequences has been falling behind. Our method may be a useful tool for supplementing functional information already automatically available. RESULTS: The method reached a level of more than 82% accuracy in a full cross-validation test. Due to a lack of functional annotations, we could infer localization for fewer than half of all proteins in SWISS-PROT. We applied LOCkey to annotate five entirely sequenced proteomes, namely Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Arabidopsis thaliana (plant) and a subset of all human proteins. LOCkey found about 8000 new annotations of sub-cellular localization for these eukaryotes.

Abstracting and Indexing↗

PubMed2002

Support vector regression applied to the determination of the developmental age of a Drosophila embryo from its segmentation gene expression patterns.

MOTIVATION: In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila. RESULTS: By applying support vector regression we have developed a fast method for automated staging of an embryo on the basis of its gene expression pattern. Support vector regression is a statistical method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed good prediction accuracy. The optimal regression function was then used for the prediction of the gene expression based age of embryos in which the precise age has not been measured by membrane morphology. Moreover, we show that the same accuracy of prediction can be achieved when the dimensionality of the feature vector was reduced by applying factor analysis. The data reduction allowed us to avoid over-fitting and to increase the efficiency of the algorithm.

Aging↗

PubMed2002

Assessment of neuropsychological function through use of the Cambridge Neuropsychological Testing Automated Battery: performance in 4- to 12-year-old children.

In this article, children's performance on subtasks from the Cambridge Neuropsychological Testing Automated Battery (CANTAB) is described. Two samples were recruited, one of which included children who spoke English as a second language. Children in this group also completed subtests from the Wechsler Intelligence Scale for Children-Third Revision (WISC-III). Despite the fact that ESL children scored over 1 SD below the norm on the WISC-III Vocabulary subtest, there were no CANTAB performance distinctions between primary versus secondary English-language speakers. In addition, several aspects of CANTAB performance were significantly correlated with verbal and nonverbal IQ. When developmental trends were examined, findings indicated that several aspects of frontal lobe function (memory span, working memory, and planning skills) are not functionally mature, by the age of 12 years. Implications for use of the CANTAB in clinical studies are discussed.

Attention↗

PubMed2003

Multiwavelet grading of pathological images of prostate.

Histological grading of pathological images is used to determine level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by non-invasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.

Algorithms↗