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PubMed2004

Automatic tracking, feature extraction and classification of C elegans phenotypes.

This paper presents a method for automatic tracking of the head, tail, and entire body movement of the nematode Caenorhabditis elegans (C. elegans) using computer vision and digital image analysis techniques. The characteristics of the worm's movement, posture and texture information were extracted from a 5-min image sequence. A Random Forests classifier was then used to identify the worm type, and the features that best describe the data. A total of 1597 individual worm video sequences, representing wild type and 15 different mutant types, were analyzed. The average correct classification ratio, measured by out-of-bag (OOB) error rate, was 90.9%. The features that have most discrimination ability were also studied. The algorithm developed will be an essential part of a completely automated C. elegans tracking and identification system.

Algorithms↗
PubMed2006

Extracting fuzzy rules from polysomnographic recordings for infant sleep classification.

A neuro-fuzzy classifier (NFC) of sleep-wake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20-s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, REM-Sleep, Non-REM Sleep Stage 1, Stage 2, and Stage 3-4. The definite criterion for a sleep state or stage to be established is duration of at least 1 min. The data set consisted of a total of 14 continuous recordings of naturally occurring naps (average duration: 143 +/- 39 min), corresponding to a total of 6021 epochs. They were divided in a training, a validation and a test set with 7, 2, and 5 recordings, respectively. During supervised training, the system determined the fuzzy concepts associated to the inputs and the rules required for performing the classification, extracting knowledge from the training set, and pruning nonrelevant rules. Results on an independent test set achieved 83.9 +/- 0.4% of expert agreement. The fuzzy rules obtained from the training examples without a priori information showed a high level of coincidence with the crisp rules stated by the experts, which are based on internationally accepted criteria. These results show that the NFC can be a valuable tool for implementing an automated sleep-wake classification system.

Algorithms↗
PubMed2005

Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes.

BACKGROUND: Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at x 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin. METHODS: In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated. RESULTS: Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist. CONCLUSIONS: The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images.

Algorithms↗
PubMedDate not supplied

Diagnostic parameters in liquid-based cervical cytology using a coagulant suspension fixative.

OBJECTIVE: To evaluate in detail the morphology of cervical cell samples suspended in the coagulant fixative BoonFix (Finetec, Tokyo, Japan) in liquid-based Papspin slides (Thermo Shandon, Pittsburgh, Pennsylvania, U.S.A) to detect shifts in diagnostic parameters for infections and neoplasia. STUDY DESIGN: Split samples of 1,010 cases were collected. All Papspin slides were scanned with neural network technology. In 849 cases the diagnosis was "within normal limits"; in 22 cases it was preneoplasia. In 151 special cases conventional smears were compared with thin-layer slides. RESULTS: In 85% of the 151 special cases, a shift of the diagnostic parameter was observed in the Papspin slide. The parameter adhesion of inflammatory cells to epithelial cells was easier to discern in 94% of the cases, and adhesion of microorganisms varied 43-100%. Koilocytosis was more visible in 79%. Prominent nucleoli in atypical and malignant cells were enhanced in 50-100% of cases with preneoplasia. The fact that the cells on the Papspin slide were no longer present in diagnostic streaks posed a problem only in the case of follicular cervicitis. CONCLUSION: The shifts in parameters facilitated the diagnostic process. BoonFix permits the screening of liquid-based Papspin slides, which have proven to be well suited to automated neural network scanning.

Bacterial Infections↗
PubMed2005

Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei.

BACKGROUND: Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous region-based methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed. METHODS: A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree. RESULTS: The proposed method was found to perform consistently better, achieving merging accuracy in the range of 92% to 100% compared with our previous algorithm, which varied in the range of 75% to 97%, even with a modest merging tree depth of 3. The overall average accuracy improved from 90% to 96%, with roughly the same computational cost for a set of representative images drawn from the CA1, CA3, and parietal cortex regions of the rat hippocampus. CONCLUSION: Hierarchical tree model-based algorithms significantly improve the accuracy of automated nuclear segmentation without sacrificing speed.

Algorithms↗
PubMed2005

A hybrid machine-learning approach for segmentation of protein localization data.

MOTIVATION: Subcellular protein localization data are critical to the quantitative understanding of cellular function and regulation. Such data are acquired via observation and quantitative analysis of fluorescently labeled proteins in living cells. Differentiation of labeled protein from cellular artifacts remains an obstacle to accurate quantification. We have developed a novel hybrid machine-learning-based method to differentiate signal from artifact in membrane protein localization data by deriving positional information via surface fitting and combining this with fluorescence-intensity-based data to generate input for a support vector machine. RESULTS: We have employed this classifier to analyze signaling protein localization in T-cell activation. Our classifier displayed increased performance over previously available techniques, exhibiting both flexibility and adaptability: training on heterogeneous data yielded a general classifier with good overall performance; training on more specific data yielded an extremely high-performance specific classifier. We also demonstrate accurate automated learning utilizing additional experimental data.

Animals↗
PubMed1982

CYBEST model 3 automated cytologic screening system for uterine cancer utilizing image analysis processing.

The improvements incorporated into the Model 3 version of CYBEST (Cyto-Biological Electronic Screening System) are highlighted. Following the successful development of a software-controlled automatic shading for a video system, the new Model 3 CYBEST contains a televisions scan system with a single-step, fine-resolution scan and strobe-light illumination in place of the two-step (coarse and fine) scan of Model 2. The automatic shading control is described in detail, as is the automated focusing system, which uses a touch-sensor to achieve a near-focus level and a differential adding algorithm to obtain exact focus. Improvements in the slide magazine and slide autochanger have quadrupled the number of slides that may be loaded into the machine at one time while increasing the speed of operation. CYBEST Model 3 has achieved our goal of rapid processing, requiring less than three minutes per specimen for final assessment as compared with the six minutes per specimen of Model 2. Field tests of Model 3 are currently under way, with a large number of smears prepared by our automated cell dispersion and monolayer smearing device (CYBEST-CDMS).

Cell Nucleus↗
PubMed2006

Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software.

Tandem mass spectrometry (MS/MS) allows for the rapid identification of many types of post-translational modifications (PTMs), especially those that can be detected by a diagnostic mass shift in one or more peptide fragment ions (for example, phosphorylation). But some PTMs (for example, SUMOs and other ubiquitin-like modifiers) themselves produce multiple fragment ions; combined with fragments from the modified target peptide, a complex overlapping fragmentation pattern is thus generated, which is uninterpretable by standard peptide sequencing software. Here we introduce SUMmOn, an automated pattern recognition tool that detects diagnostic PTM fragment ion series within complex MS/MS spectra, to identify modified peptides and modification sites within these peptides. Using SUMmOn, we demonstrate for the first time that human SUMO-1 multimerizes in vitro primarily via three N-terminal lysines, Lys7, Lys16 and Lys17. Notably, our method is theoretically applicable to any type of modification or chemical moiety generating a unique fragment ion pattern.

Algorithms↗
PubMed2005

Vertebral shape: automatic measurement with dynamically sequenced active appearance models.

The shape and appearance of vertebrae on lateral dual x-ray absorptiometry (DXA) scans were statistically modelled. The spine was modelled by a sequence of overlapping triplets of vertebrae, using Active Appearance Models (AAMs). To automate vertebral morphometry, the sequence of trained models was matched to previously unseen scans. The dataset includes a significant number of pathologies. A new dynamic ordering algorithm was assessed for the model fitting sequence, using the best quality of fit achieved by multiple sub-model candidates. The accuracy of the search was improved by dynamically imposing the best quality candidate first. The results confirm the feasibility of substantially automating vertebral morphometry measurements even with fractures or noisy images.

Algorithms↗
PubMed2004

The classification of oesophageal 24 h pH measurements using a Kohonen self-organizing feature map.

Analysis of 24 h oesophageal pH studies can be problematic with many patients asymptomatic during the investigation, despite observations of reflux. The aim of this study was to carry out a cluster analysis of ambulatory pH studies to determine any underlying patterns and classes within the data. The results of 900 24 h pH studies were investigated using the Kohonen self-organizing feature map (SOFM), a neural network that can be used to identify clusters within multidimensional data. The clinical features were presented to the network and the main classes identified. The SOFM-based analysis showed that patients clinically assessed as having symptomatic reflux during the study could be described by four major classifications. The results also showed that the probability of identifying a correlation between symptoms and reflux during an investigation varies from 0.49 to 0.78 for the classes identified. The developed network may be a useful tool in the classification of pH data. The cluster-based technique may offer an alternative to standard statistical techniques for high-dimensional gastrointestinal data and form the basis of an expert system for the automated analysis of pH data.

Algorithms↗
PubMed2006

Adaptive AR and neurofuzzy approaches: access to cerebral particle signatures.

In recent years, a relationship has been suggested between the occurrence of cerebral embolism and stroke. Ultrasound has therefore become essential in the detection of emboli when monitoring cerebral vascular disorders and forms part of ultrasound brain-imaging techniques. Such detection is based on investigating the middle cerebral artery using a TransCranial Doppler (TCD) system, and analyzing the Doppler signal of the embolism. Most of the emboli detected in practical experiments are large emboli because their signatures are easy to recognize in the TCD signal. However, detection of small emboli remains a challenge. Various approaches have been proposed to solve the problem, ranging from the exclusive use of expert human knowledge to automated collection of signal parameters. Many studies have recently been performed using time-frequency distributions and classical parameter modeling for automatic detection of emboli. It has been shown that autoregressive (AR) modeling associated with an abrupt change detection technique is one of the best methods for detection of microemboli. One alternative to this is a technique based on taking expert knowledge into account. This paper aims to unite these two approaches using AR modeling and expert knowledge through a neurofuzzy approach. The originality of this approach lies in combining these two techniques and then proposing a parameter referred to as score ranging from 0 to 1. Unlike classical techniques, this score is not only a measure of confidence of detection but also a tool enabling the final detection of the presence or absence of microemboli to be performed by the practitioner. Finally, this paper provides performance evaluation and comparison with an automated technique, i.e., AR modeling used in vitro.

Algorithms↗
PubMed2005

Computer-assisted pattern recognition of autoantibody results.

Immunoassay-based anti-nuclear antibody (ANA) screens are increasingly used in the initial evaluation of autoimmune disorders, but these tests offer no "pattern information" comparable to the information from indirect fluorescence assay-based screens. Thus, there is no indication of "next steps" when a positive result is obtained. To improve the utility of immunoassay-based ANA screening, we evaluated a new method that combines a multiplex immunoassay with a k nearest neighbor (kNN) algorithm for computer-assisted pattern recognition. We assembled a training set, consisting of 1,152 sera from patients with various rheumatic diseases and non-diseased patients. The clinical sensitivity and specificity of the multiplex method and algorithm were evaluated with a test set that consisted of 173 sera collected at a rheumatology clinic from patients diagnosed by using standard criteria, as well as 152 age- and sex-matched sera from presumably healthy individuals (sera collected at a blood bank). The test set was also evaluated with a HEp-2 cell-based enzyme-linked immunosorbent assay (ELISA). Both the ELISA and multiplex immunoassay results were positive for 94% of the systemic lupus erythematosus (SLE) patients. The kNN algorithm correctly proposed an SLE pattern for 84% of the antibody-positive SLE patients. For patients with no connective tissue disease, the multiplex method found fewer positive results than the ELISA screen, and no disease was proposed by the kNN algorithm for most of these patients. In conclusion, the automated algorithm could identify SLE patterns and may be useful in the identification of patients who would benefit from early referral to a specialist, as well as patients who do not require further evaluation.

Algorithms↗
PubMed2005

Investigation of selected baseline removal techniques as candidates for automated implementation.

Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as 'baseline', 'background', and/or 'background noise'. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.

Algorithms↗
PubMed2001

LAVA--the system for all-ceramic ZrO2 crown and bridge frameworks.

All-ceramic restorations in the posterior region are an increasingly important area of dental care. However, no real suitable ceramics or economic processing procedures have been available so far. With the new LAVA system, it will be possible to satisfy these demands in the future. The system is based on the machining of presintered zirconia, which, due to its outstanding mechanical properties, its biocompatibility, and its excellent esthetics in combination with a specially designed veneer ceramic, is the ideal candidate for these applications. In combination with a corresponding CAD/CAM unit, the use of an easy-to-machine presintered ceramic material (which is sintered to full density after shaping, thus eliminating the need for extensive use of diamond tools) allows for the first time reliable, fully-automated and thus fast manufacturing of such restorations.

Cementation↗
PubMed2005

Markov model recognition and classification of DNA/protein sequences within large text databases.

MOTIVATION: Short sequence patterns frequently define regions of biological interest (binding sites, immune epitopes, primers, etc.), yet a large fraction of this information exists only within the scientific literature and is thus difficult to locate via conventional means (e.g. keyword queries or manual searches). We describe herein a system to accurately identify and classify sequence patterns from within large corpora using an n-gram Markov model (MM). RESULTS: As expected, on test sets we found that identification of sequences with limited alphabets and/or regular structures such as nucleic acids (non-ambiguous) and peptide abbreviations (3-letter) was highly accurate, whereas classification of symbolic (1-letter) peptide strings with more complex alphabets was more problematic. The MM was used to analyze two very large, sequence-containing corpora: over 7.75 million Medline abstracts and 9000 full-text articles from Journal of Virology. Performance was benchmarked by comparing the results with Journal of Virology entries in two existing manually curated databases: VirOligo and the HLA Ligand Database. Performance estimates were 98 +/- 2% precision/84% recall for primer identification and classification and 67 +/- 6% precision/85% recall for peptide epitopes. We also find a dramatic difference between the amounts of sequence-related data reported in abstracts versus full text. Our results suggest that automated extraction and classification of sequence elements is a promising, low-cost means of sequence database curation and annotation. AVAILABILITY: MM routine and datasets are available upon request.

Abstracting and Indexing↗