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PubMed2004

Novel genetic-neuro-fuzzy filter for speckle reduction from sonography images.

Edge-preserving speckle noise reduction is essential to computer-aided ultrasound image processing and understanding. A new class of genetic-neuro-fuzzy filter is proposed to optimize the trade-off between speckle noise removal and edge preservation. The proposed approach combines the advantages of the fuzzy, neural, and genetic paradigms. Neuro-fuzzy approaches are very promising for nonlinear filtering of noisy images. Fuzzy reasoning embedded into the network structure aims at reducing errors while fine details are being processed. The learning method based on the real-time genetic algorithms (GAs) performs an effective training of the network from a collection of training data and yields satisfactory results after a few generations. The performance of the proposed filter has been compared with that of the commonly used median and Wiener filters in reducing speckle noises on ultrasound images. We evaluate this filter by passing the filter's output to the edge detection algorithm and observing its ability to detect edge pixels.Experimental results show that the proposed genetic-neuro-fuzzy technique is very effective in speckle noise reduction as well as detail preserving even in the presence of highly noise corrupted data, and it works significantly better than other well-known conventional methods in the literature.

Algorithms↗
PubMed2004

Gram-staining characterisation of activated sludge filamentous bacteria by automated colour analysis.

An automated image analysis method has been developed for the monitoring of the Gram-staining characteristics of filamentous bacteria in activated sludge. The binary method of pixel classification agreed with manual estimation (level of correlation of 0.9 for Gram-positive bacteria). Its robustness has been assessed by repeatability tests. Population shifts in terms of Gram-staining characteristics have been monitored in laboratory-scale experiments with two feeding schedules using this technique.

Colony Count, Microbial↗
PubMed2005

Risk-based prioritization of air pollution monitoring using fuzzy synthetic evaluation technique.

Air pollution monitoring programs aim to monitor pollutants and their probable adverse effects at various locations over concerned area. Either sensitivity of receptors/location or concentration of pollutants is used for prioritizing the monitoring locations. The exposure-based approach prioritizes the monitoring locations based on population density and/or location sensitivity. The hazard-based approach prioritizes the monitoring locations using intensity (concentrations) of air pollutants at various locations. Exposure and hazard-based approaches focus on frequency (probability of occurrence) and potential hazard (consequence of damage), respectively. Adverse effects should be measured only if receptors are exposed to these air pollutants. The existing methods of monitoring location prioritization do not consider both factors (hazard and exposure) at a time. Towards this, a risk-based approach has been proposed which combines both factors: exposure frequency (probability of occurrence/exposure) and potential hazard (consequence). This paper discusses the use of fuzzy synthetic evaluation technique in risk computation and prioritization of air pollution monitoring locations. To demonstrate the application, common air pollutants like CO, NOX, PM10 and SOX are used as hazard parameters. Fuzzy evaluation matrices for hazard parameters are established for different locations in the area. Similarly, fuzzy evaluation matrices for exposure parameters: population density, location and population sensitivity are also developed. Subsequently, fuzzy risk is determined at these locations using fuzzy compositional rules. Finally, these locations are prioritized based on defuzzified risk (crisp value of risk, defined as risk score) and the five most important monitoring locations are identified (out of 35 potential locations). These locations differ from the existing monitoring locations.

Air Pollutants↗
PubMed2006

Hierarchical clustering analysis of flexible GBR 12909 dialkyl piperazine and piperidine analogs.

Pharmacophore modeling of large, drug-like molecules, such as the dopamine reuptake inhibitor GBR 12909, is complicated by their flexibility. A comprehensive hierarchical clustering study of two GBR 12909 analogs was performed to identify representative conformers for input to three-dimensional quantitative structure-activity relationship studies of closely-related analogs. Two data sets of more than 700 conformers each produced by random search conformational analysis of a piperazine and a piperidine GBR 12909 analog were studied. Several clustering studies were carried out based on different feature sets that include the important pharmacophore elements. The distance maps, the plot of the effective number of clusters versus actual number of clusters, and the novel derived clustering statistic, percentage change in the effective number of clusters, were shown to be useful in determining the appropriate clustering level. Six clusters were chosen for each analog, each representing a different region of the torsional angle space that determines the relative orientation of the pharmacophore elements. Conformers of each cluster that are representative of these regions were identified and compared for each analog. This study illustrates the utility of using hierarchical clustering for the classification of conformers of highly flexible molecules in terms of the three-dimensional spatial orientation of key pharmacophore elements.

Cluster Analysis↗
PubMed2005

Classification of aorta insufficiency and stenosis using neuro-fuzzy system.

Cardiac Doppler signals recorded from aorta valve of 60 patients were transferred to a personal computer by using a 16 bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at jet blood flows such as cardiac Doppler signals, it sometimes causes wrong interpretation. In order to do a good interpretation and rapid diagnosis, cardiac Doppler blood flow signals were statistically arranged and then classified using neuro-fuzzy system. The NEFCLASS model, which is used to create a fuzzy classification system from data, was used. The classification results show that neuro-fuzzy system offers best results in the case of diagnosis.

Aortic Valve Insufficiency↗
PubMed2006

A robust generalized fuzzy operator approach to film contrast correction in digital subtraction radiography.

Digital subtraction radiography requires close matching of the contrast in each pair of X-ray images to be subtracted. Previous studies have shown that nonparametric contrast/brightness correction methods using the cumulative density function (CDF) and its improvements, which are based on gray-level transformation associated with the pixel histogram, perform well in uniform contrast/brightness difference conditions. However, for radiographs with nonuniform contrast/ brightness, the CDF produces unsatisfactory results. In this paper, we propose a new approach in contrast correction based on the generalized fuzzy operator with least square method. The result shows that 50% of the contrast/brightness errors can be corrected using this approach when the contrast/brightness difference between a radiographic pair is 10 U. A comparison of our approach with that of CDF is presented, and this modified GFO method produces better contrast normalization results than the CDF approach.

Fuzzy Logic↗
PubMed2006

Adaptive certainty-based classification for decomposition of EMG signals.

An adaptive certainty-based supervised classification approach for electromyographic (EMG) signal decomposition is presented and evaluated. Similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit (MU) firing pattern information: passive and active. Performance of the developed classifier was evaluated using synthetic signals of known properties and real signals and compared with the performance of the certainty classifier (CC). Across the sets of simulated and real EMG signals used for comparison, the adaptive certainty classifier (ACC) had both better average performance and lower performance variability. For simulated signals of varying intensity, the ACC had an average correct classification rate (CCr ) of 83.7% with a mean absolute deviation (MAD) of 5.8% compared to 78.3 and 8.7%, respectively, for the CC. For simulated signals with varying amounts of shape and/or firing pattern variability, the ACC had a CCr of 79.7% with a MAD of 4.7% compared to 76.6 and 6.9%, respectively, for the CC. For real signals, the ACC had a CCr of 70.0% with a MAD of 6.3% compared to 64.9 and 6.4%, respectively, for the CC. The test results demonstrate that the ACC can manage both MUP shape variability as well as MU firing pattern variability. The ACC adapts to EMG signal characteristics to create dynamic data driven classification criteria so that the number of MUP assignments made reflects the signal complexity and the number of erroneous assignments is kept sufficiently low. The ability of the ACC to adjust to specific signal characteristics suggests that it can be successfully applied to a wide variety of EMG signals.

Computer Simulation↗
PubMed2006

Image reconstruction for positron emission tomography using fuzzy nonlinear anisotropic diffusion penalty.

Iterative algorithms such as maximum likelihood-expectation maximization (ML-EM) become the standard for the reconstruction in emission computed tomography. However, such algorithms are sensitive to noise artifacts so that the reconstruction begins to degrade when the number of iterations reaches a certain value. In this paper, we have investigated a new iterative algorithm for penalized-likelihood image reconstruction that uses the fuzzy nonlinear anisotropic diffusion (AD) as a penalty function. The proposed algorithm does not suffer from the same problem as that of ML-EM algorithm, and it converges to a low noisy solution even if the iteration number is high. The fuzzy reasoning instead of a nonnegative monotonically decreasing function was used to calculate the diffusion coefficients which control the whole diffusion. Thus, the diffusion strength is controlled by fuzzy rules expressed in a linguistic form. The proposed method makes use of the advantages of fuzzy set theory in dealing with uncertain problems and nonlinear AD techniques in removing the noise as well as preserving the edges. Quantitative analysis shows that the proposed reconstruction algorithm is suitable to produce better reconstructed images when compared with ML-EM, ordered subsets EM (OS-EM), Gaussian-MAP, MRP, TV-EM reconstructed images.

Algorithms↗
PubMed2006

Fuzzy polynucleotide spaces and metrics.

The study of genetic sequences is of great importance in biology and medicine. Mathematics is playing an important role in the study of genetic sequences and, generally, in bioinformatics. In this paper, we extend the work concerning the Fuzzy Polynucleotide Space (FPS) introduced in Torres, A., Nieto, J.J., 2003. The fuzzy polynucleotide Space: Basic properties. Bioinformatics 19(5); 587-592 and Nieto, J.J., Torres, A., Vazquez-Trasande, M.M. 2003. A metric space to study differences between polynucleotides. Appl. Math. Lett. 27:1289-1294: by studying distances between nucleotides and some complete genomes using several metrics. We also present new results concerning the notions of similarity, difference and equality between polynucleotides. The results are encouraging since they demonstrate how the notions of distance and similarity between polynucleotides in the FPS can be employed in the analysis of genetic material.

Algorithms↗
PubMed1993

An aggregation of pro and con evidence for medical decision support systems.

One promising way to increase the classification accuracy of medical decision support systems is to implement heuristic combinations of pattern recognition and artificial intelligence tools. A parallel between "cognition" model and differential diagnostic task is sketched accentuating the aggregation of activating and restraining inputs and corresponding PRO and CON evidence in medicine. On the basis of this paradigm a trainable model of a fuzzy neuron is proposed which resembles some elements from the physician's decision process. An example from aviation medicine is presented which demonstrates the enhanced performance.

Aerospace Medicine↗
PubMed1994

Neonatal assessment using the Apgar fuzzy expert system.

We applied fuzzy theory to the Apgar scoring system (APG) to devise an Apgar fuzzy expert system (AFES). Three AFESs were determined separately by each one of three doctor groups (four inexperienced obstetricians, four experienced obstetricians and four expert neonatologists) in order to demonstrate that the AFES reflected the examiner's expertise and situation. Two-hundred and sixty-seven neonates were assessed 1 min after birth by an experienced obstetrician using the APG and the three AFESs. Statistical analysis of the relationship between the APG and the three AFESs in the acidosis group showed that the AFES determined by four expert neonatologists had the highest sensitivity and were significantly different (p < 0.05) from the APG. The AFES determined by the four expert neonatologists was revealed to be a valuable tool.

Acidosis↗
PubMed1996

A neuro-fuzzy algorithm for diagnosis of coronary artery stenosis.

In this paper a method of fuzzy decision making applied to diagnosis of coronary artery stenosis is presented. The method uses a neural network approach for the diagnosis of stenosis in the three main coronary arteries (left anterior descending, right coronary artery, and circumflex). First, the knowledge base domain, 201Tl scintigram training data, is explained and the method of preprocessing the original heart images is given. Next, the method of dealing with the uncertainties present in the data using the fuzzy approach is outlined. Finally, the algorithm and the results are discussed and compared with other approaches.

Algorithms↗
PubMed1993

A rule-based postoperative pain controller: simulation results.

We describe a system for controlling postoperative pain, a phenomenon that is difficult to treat by conventional control methods due to interpatient variability, interferences, non-linearity and the lack of a plausible, well-defined mathematical model. The system consists of two phases. In Phase 1 a closed-loop fuzzy controller implementing a suitable control strategy brings the patient to a zero-pain state. In Phase 2, an open-loop computer-assisted continuous infusion controller maintains a constant concentration of the analgesic (alfentanil) in plasma, subject to an upper safety limit on infusion rate; the set-point of this controller is periodically revised (either maintained or reduced) on the basis of feedback on the duration of zero pain (set-point reduction is necessary because the open-loop system has no means of knowing whether analgesic is accumulating in the patient). Pain is quantified by the patient on a numerical scale of 1 to 10 at 1.5-min intervals during Phase 1 and 9-min intervals during Phase 2. In simulation trials in which a fixed approximate model was used for the effect of sedation on pain while the pharmacokinetics of alfentanil were varied from one simulated patient to another, zero pain was achieved in under 15 min with minimal overshoot in plasma drug concentration and was maintained, with only minor deviation, by means of low drug concentrations.

Alfentanil↗
PubMed1995

Bacterial colony counting using distance transform.

A distance-transform based technique is presented for the segmentation of monochrome images of colonies grown on membrane filters. This is used to count the number of Escherichia coli in a given water sample, which is used as a parameter for determining water quality. The result is compared with fuzzy c-means clustering approach.

Algorithms↗
PubMed1994

A decision support system for AIDS intervention and prevention.

In recent years, the importance of information systems has been identified as a vital issue to continuing success in AIDS intervention and prevention (AIP). The advances in information technology have resulted in integrative information systems including decision support systems (DSS). The concept of DSS for AIP was created at the intersection of two trends. The first trend was a growing belief that AIP information systems are successful in automating operations in AIP programs. The second was a continuing improvement in modeling and software development in the AIP area. This paper presents an integrated DSS for AIP. The system is integrated with a database and achieves its efficiency by incorporating various algorithms and models to support AIP decision processes. The application examples include screening AIDS-risky behaviors, evaluating educational interventions, and scheduling AIP sessions. The implementation results present evidence of the usefulness of the system in AIP.

AIDS Serodiagnosis↗
PubMed1995

Automatic detection of auditory brainstem responses using feature vectors.

The efficiency of several methods for the automatic detection of near threshold auditory brainstem responses (ABRs), based on feature vectors, was assessed on a sample of 374 recordings obtained from neonates. Features are quantitative descriptors of different aspects of the response commonly taken into consideration by expert ABR judges, such as: similarity with a template, reliability of the response, and intrinsic morphological characteristics. The classification methods used were: comparisons of individual features with a threshold, linear discriminant functions based on combination of features, artificial neural networks (ANNs) based on the raw ABR, and ANNs based on combinations of features. The network used was a modified version of the Fuzzy ART Map model. The accuracy of the classification into normal and abnormal (as previously scored by a panel of experts) was assessed with methods from signal detection theory. Our results showed that the approaches based on feature vectors (discriminant function and ANN) performed more efficiently than the ANN with raw data, or the individual features.

Algorithms↗
PubMed1995

Extraction of fuzzy rules using neural networks with structure level adaptation--verification to the diagnosis of hepatobiliary disorders.

This paper presents the reasoning and learning method for fuzzy rules using structure level adaptation of neural networks. In a usual neural network mechanism, we can observe some behaviors during the learning process. Based on such behaviors of neuron activity, we can generate or annihilate the specified neurons respectively in hidden layer to achieve an overall good system. In the method that we have proposed, we have used a procedure to derive the neuron generation/annihilation automatically, and applied such a procedure to the learning system where the experimental data related to hepatobiliary disorders were used. After learning by using randomly chosen data, the proposed system correctly diagnosed over 70% of cases. According to these results, we can find that fuzzy rules have some relationship with the degree of the input weight vector. As a result, we can assume that fuzzy rules for hepatobiliary disorders are extracted from this learned network.

Algorithms↗
PubMed1996

An integrated rule- and case-based approach to AIDS initial assessment.

The traditional approach to the development of knowledge-based systems (KBS) has been rule-based, where heuristic knowledge is encoded in a set of production rules. A rule-based reasoning (RBR) system needs a well constructed domain theory as its reasoning basis, and it does not make substantial use of the knowledge embedded in previous cases. An RBR system performs relatively well in a knowledge-rich application environment. Although its capability may be limited when previous experiences are not a good representation of the whole population, a case-based reasoning (CBR) system is capable of using past experiences as problem solving tools, therefore, it is appropriate for an experience-rich domain. In recent years, both RBR and CBR have emerged as important and complementary reasoning methodologies in artificial intelligence. For problem solving in AIDS intervention and prevention, it is useful to integrate RBR and CBR. In this paper, a hybrid KBS which integrates a deductive RBR system and an inductive CBR system is proposed to assess AIDS-risky behaviors.

Acquired Immunodeficiency Syndrome↗