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PubMed2006

A fully automated method for quantifying and localizing white matter hyperintensities on MR images.

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.

Aged↗
PubMedDate not supplied

Visual-auditory integration during speech imitation in autism.

Children with autistic spectrum disorder (ASD) may have poor audio-visual integration, possibly reflecting dysfunctional 'mirror neuron' systems which have been hypothesised to be at the core of the condition. In the present study, a computer program, utilizing speech synthesizer software and a 'virtual' head (Baldi), delivered speech stimuli for identification in auditory, visual or bimodal conditions. Children with ASD were poorer than controls at recognizing stimuli in the unimodal conditions, but once performance on this measure was controlled for, no group difference was found in the bimodal condition. A group of participants with ASD were also trained to develop their speech-reading ability. Training improved visual accuracy and this also improved the children's ability to utilize visual information in their processing of speech. Overall results were compared to predictions from mathematical models based on integration and non-integration, and were most consistent with the integration model. We conclude that, whilst they are less accurate in recognizing stimuli in the unimodal condition, children with ASD show normal integration of visual and auditory speech stimuli. Given that training in recognition of visual speech was effective, children with ASD may benefit from multi-modal approaches in imitative therapy and language training.

Adolescent↗
PubMed2005

An inexact fuzzy two-stage stochastic model for quantifying the efficiency of nonpoint source effluent trading under uncertainty.

Reduction of nonpoint source (NPS) pollution from agricultural lands is a major concern in most countries. One method to reduce NPS pollution is through land retirement programs. This method, however, may result in enormous economic costs especially when large sums of croplands need to be retired. To reduce the cost, effluent trading can be employed to couple with land retirement programs. However, the trading efforts can also become inefficient due to various uncertainties existing in stochastic, interval, and fuzzy formats in agricultural systems. Thus, it is desired to develop improved methods to effectively quantify the efficiency of potential trading efforts by considering those uncertainties. In this respect, this paper presents an inexact fuzzy two-stage stochastic programming model to tackle such problems. The proposed model can facilitate decision-making to implement trading efforts for agricultural NPS pollution reduction through land retirement programs. The applicability of the model is demonstrated through a hypothetical effluent trading program within a subcatchment of the Lake Tai Basin in China. The study results indicate that the efficiency of the trading program is significantly influenced by precipitation amount, agricultural activities, and level of discharge limits of pollutants. The results also show that the trading program will be more effective for low precipitation years and with stricter discharge limits.

Agriculture↗
PubMed2006

Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps.

There is concern among Aboriginal communities in Canada that conventional approaches to the treatment of diabetes are ineffective in part because they fail to recognize the local Aboriginal perspective on the causal determinants of diabetes. While this shortcoming has been recognized, there have been no explicit attempts to practically define these perspectives and prescribe how conventional health management might be altered to incorporate them. In part, this may be due to difficulties in communicating Aboriginal perspectives in a manner which permits incorporation into conventional science-based frameworks. Here we use fuzzy cognitive mapping (FCM) as a technique to represent and compare Canadian Aboriginal and conventional science perspectives on the determinants of diabetes as contained in published articles drawn from a search of Medline and PubMed (1966-2005). The FCM allows for a detailed description of the complex system of culture, spirituality and balance at the root of the Aboriginal view. It also highlights how, for these less tangible factors, it is possible to identify more concrete stressors and outcomes which are amenable to management and monitoring. This preliminary comparison of conventional and Aboriginal views also demonstrates the potential for FCM as a technique to extract, compare and integrate perspectives of different knowledge systems into health management and policy development.

Attitude to Health↗
PubMed2005

Sonohistology for the computerized differentiation of parotid gland tumors.

A system for the computerized differentiation of parotid gland tumors is proposed. The parotid gland is the largest of the salivary glands. It is found in the subcutaneous tissue of the face, overlying the mandibular ramus and anterior and inferior to the external ear. The classification system is based on a multifeature tissue characterization approach, using fuzzy inference systems as higher-order classifiers. Baseband ultrasonic echo data were acquired during conventional ultrasound imaging examinations using standard ultrasound equipment. Several tissue-describing parameters were calculated within numerous small regions of interest to evaluate spectral and textural tissue properties. The parameters were processed by an adaptive network-based fuzzy inference system, using the results of conventional histology after parotidectomy as the "gold standard." The results of the classification are presented as a numerical score indicating the probability of a certain tumor or alteration for each parotid gland. The score can be presented to the physician during examination of the patient to improve the differentiation between various types of parotid gland tumors. The system was evaluated on n = 23 cases of patients undergoing radical parotidectomy. The receiver operating characteristic curve area is A(ROC) = 0.95 +/- 0.07 when using fourfold cross-validation over cases and differentiating between various benign parotid gland tumors and monomorphic adenoma.

Adenoma↗
PubMed2005

Municipal solid waste landfill siting using intelligent system.

Historically, landfills have been the dominant alternative for the ultimate disposal of municipal solid waste. This paper addresses the problem of siting a new landfill using an intelligent system based on fuzzy inference. The proposed system can accommodate new information on the landfill site selection by updating its knowledge base. Several factors are considered in the siting process including topography and geology, natural resources, socio-cultural aspects, and economy and safety. The system will rank sites on a scale of 0-100%, with 100% being the most appropriate one. A weighting system is used for all of the considered factors. The results from testing the system using different sites show the effectiveness of the system in the selection process.

Decision Making↗
PubMed2005

Interactive fuzzy optimization for an economic and environmental balance in a river system.

An interactive fuzzy approach is applied to develop a water quality management plan in a river basin for solving multi-objective optimization problems involving vague and imprecise information related to data, model formulation, and the decision maker's preferences. This approach, originally proposed by Sakawa in 1983, is modified and presented as a sustainable water quality management strategy in which the decision makers and the environmental analysts put forward their views on three major economic and environmental factors: river water quality, assimilative capacity, and treatment cost of wastewater. This methodology is illustrated in a case study of multi-objective water quality management in the Tou-Chen River Basin in northern Taiwan. The incorporation of these three economic and environmental factors demonstrates the capability of the fuzzy interactive multi-objective optimization approach and also works suitably in water quality management in a river basin by the trade-off procedures.

Conservation of Natural Resources↗
PubMed2005

A fuzzy index model for trophic status evaluation of reservoir waters.

An index model for quality evaluation based on the formula of similarity membership functions in the fuzzy c-means (FCM) clustering algorithm is proposed. Summing up the weighted similarity degrees between an observation and designed specific quality-ordered levels develops an alternative overall index. Stretching the values of the controlling parameters in the formula of the similarity membership functions causes diverse patterns of overall index models. Applying this proposed fuzzy index model to the trophic evaluation of reservoir waters is studied to demonstrate the practical application of this index. Every measurement of the variables is standardized by the membership function of quality evaluation on the interval [0,1], referring to the trophic status clarified in the Carlson Trophic State Index. The sensitivity analyses are studied both in the proposed index system and the Carlson Trophic State Index. Besides, a case study of the trophic status evaluation of the Feitsui Reservoir from 1987 to 2003 is presented to demonstrate the feasibility of applying the proposed evaluation model.

Chlorophyll↗
PubMed2006

Control of SBR switching by fuzzy pattern recognition.

The sequencing batch reactor (SBR) is a widely used process for biological removal of nutrients (nitrogen and phosphorus) from wastewater. It is based on the metabolism of specialised bacteria, which under alternate anaerobic/aerobic conditions uptake phosphorus and perform denitrification. Intermittent operation is normally operated on a fixed switching schedule with ample margin for possible inaccuracies, with the result that the process operation is highly inefficient. This paper proposes a switching strategy based on the indirect observation of process state through simple physico-chemical measurements and the use of an inferential engine to determine the most appropriate switching schedule. In this way the duration of each phase is limited to the time strictly necessary for the actual loading conditions. Experimental results show that the treatment cycle can be significantly shortened, with the results that more wastewater can be treated. The switching strategy is based on innovative data-processing techniques applied to simple process signals including pH, oxido-reduction potential (ORP) and dissolved oxygen (DO). They include wavelet filtering for signal denoising and fuzzy clustering for features extraction and decision-making. The formation of a knowledge-base and its adaptation during the operation are also discussed.

Aerobiosis↗
PubMed1995

Fuzzy cluster analysis of positive stress tests, a new method of combining exercise test variables to predict extent of coronary artery disease.

Fuzzy set theory is useful in the analysis of data having a graded degree of abnormality. Previous studies using sharp cutoff points between normality and abnormality have resulted in general guidelines for the interpretation of positive stress tests, but do not enable the clinician to simultaneously combine several stress test variables, each having a range of abnormality. In this study, positive stress test results from 109 patients were reviewed. An angiogram recorded within 1 month of the stress tests showed that 100 patients had coronary artery disease (CAD) (15 had left main CAD, and 27 had 3-vessel, 30 had 2-vessel, and 28 had 1-vessel disease) and 9 were normal. Six variables were selected for fuzzy cluster analysis: ST-segment change, difference between resting systolic and peak exercise systolic blood pressure, total treadmill time, peak exercise heart rate as a percentage of 100% predicted maximum for age, time to onset of angina, and duration of repolarization abnormalities. The analysis used a similarity measure to compute how closely each stress test resembled a prototypical mildly, moderately, or severely abnormal stress test. Stress tests classified by this method showed better correlation with the extent of CAD than the degree of ST-segment depression alone. Unlike tests with mild degrees of ST depression (0.5 to 1.5 mm), tests classified as mild by the method virtually excluded high-grade CAD.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1997

Cerebral hypoxia during cardiopulmonary bypass: a magnetic resonance imaging study.

BACKGROUND: Neurocognitive deficits after open heart operations have been correlated to jugular venous oxygen desaturation on rewarming from hypothermic cardiopulmonary bypass (CPB). Using a porcine model, we looked for evidence of cerebral hypoxia by magnetic resonance imaging during CPB. Brain oxygenation was assessed by T2*-weighted imaging, based on the blood oxygenation level-dependent effect (decreased T2*-weighted signal intensity with increased tissue concentrations of deoxyhemoglobin). METHODS: Pigs were placed on normothermic CPB, then cooled to 28 degrees C for 2 hours of hypothermic CPB, then rewarmed to baseline temperature. T2*-weighted, imaging was undertaken before CPB, during normothermic CPB, at 30-minute intervals during hypothermic CPB, after rewarming, and then 15 minutes after death. Imaging was with a Bruker 7.0 Tesla, 40-cm bore magnetic resonance scanner with actively shielded gradient coils. Regions of interest from the magnetic resonance images were analyzed to identify parenchymal hypoxia and correlated with jugular venous oxygen saturation. Post-hoc fuzzy clustering analysis was used to examine spatially distributed regions of interest whose pixels followed similar time courses. Attention was paid to pixels showing decreased T2* signal intensity over time. RESULTS: T2* signal intensity decreased with rewarming and in five of seven experiments correlated with the decrease in jugular venous oxygen saturation. T2* imaging with fuzzy clustering analysis revealed two diffusely distributed pixel groups during CPB. One large group of pixels (50% +/- 13% of total pixel count) showed increased T2* signal intensity (well-oxygenated tissue) during hypothermia, with decreased intensity on rewarming. Changes in a second group of pixels (34% +/- 8% of total pixel count) showed a progressive decrease in T2* signal intensity, independent of temperature, suggestive of increased brain hypoxia during CPB. CONCLUSIONS: Decreased T2* signal intensity in a diffuse spatial distribution indicates that a large proportion of cerebral parenchyma is hypoxic (evidenced by an increased proportion of tissue deoxyhemoglobin) during CPB in this porcine model. Neuronal damage secondary to parenchymal hypoxia may explain the postoperative neuropsychological dysfunction after cardiac operations.

Animals↗
PubMed2001

Adaptive frequency decomposition of EEG with subsequent expert system analysis.

We present a hybrid system for automatic analysis of clinical routine EEG, comprising a spectral analysis and an expert system. EEG raw data are transformed into the time-frequency domain by the so-called adaptive frequency decomposition. The resulting frequency components are converted into pseudo-linguistic facts via fuzzification. Finally, an expert system applies symbolic rules formulated by the neurologist to evaluate the extracted EEG features. The system detects artefacts, describes alpha rhythm by frequency, amplitude, and stability and after artefact rejection detects pathologic slow activity. All results are displayed as linguistic terms, numerical values and maps of temporal extent, giving an overview about the clinical routine EEG.

Adolescent↗
PubMed2002

Application of FFT analyzed cardiac Doppler signals to fuzzy algorithm.

Doppler signals, recorded from the output of tricuspid, mitral, and aorta valves of 60 patients, were transferred to a personal computer via 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 highly turbulent blood flows, it sometimes leads to wrong interpretation of cardiac Doppler signals. In order to avoid this problem, firstly six known diseased heart signals such as hypertension, mitral stenosis, mitral failure, tricuspid stenosis, aorta stenosis, aorta insufficiency were introduced to fuzzy algorithm. Then, the unknown heart diseases from 15 patients were applied to the same fuzzy algorithm in order to detect the kinds of diseases. It is observed that the fuzzy algorithm gives true results for detecting the kind of diseases.

Algorithms↗
PubMed2004

Intelligent training system integrated in an echocardiography simulator.

Computer simulators play an important role in medical education. We have extended our simulator EchoComJ with an intelligent training system (ITS) to support trainees adjusting echocardiographic standard views. EchoComJ is an augmented reality application that combines real three-dimensional ultrasound data with a virtual heart model enabling one to simulate an echocardiographic examination. The ITS analyzes the image planes according to their position, orientation and the visualization of anatomical landmarks using fuzzy rules. An adaptive feedback is provided that colors the specific anatomic landmarks within the contours of the virtual model based on the quality of the image plane.

Artificial Intelligence↗
PubMed1999

An algorithm for detecting features of the hormone profiles of the human menstrual cycle.

An algorithm for detecting features of the cycles of the gonadotropic and ovarian hormones in women is described. The algorithm can detect hormone peaks and normal cycles defined in terms of the peaks in sequences of measurements that have an arbitrary starting point in the menstrual cycle and are of arbitrary length. The algorithm makes use of fuzzy set theory and is optimized using signal detection theory.

Algorithms↗
PubMed2000

Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering.

A method is presented for fully automated detection of Multiple Sclerosis (MS) lesions in multispectral magnetic resonance (MR) imaging. Based on the Fuzzy C-Means (FCM) algorithm, the method starts with a segmentation of an MR image to extract an external CSF/lesions mask, preceded by a local image contrast enhancement procedure. This binary mask is then superimposed on the corresponding data set yielding an image containing only CSF structures and lesions. The FCM is then reapplied to this masked image to obtain a mask of lesions and some undesired substructures which are removed using anatomical knowledge. Any lesion size found to be less than an input bound is eliminated from consideration. Results are presented for test runs of the method on 10 patients. Finally, the potential of the method as well as its limitations are discussed.

Algorithms↗
PubMed2000

Pattern classification by a neurofuzzy network: application to vibration monitoring.

An innovative neurofuzzy network is proposed herein for pattern classification applications, specifically for vibration monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, on-line, and incremental learning algorithm. This network can be considered a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. The classification performance of the proposed neurofuzzy network is validated on the Fisher's Iris data, which is a well-known benchmark data set. For the generalization capability, the neurofuzzy network can achieve 97.33% correct classification. In addition, to demonstrate the efficiency and effectiveness of the proposed neurofuzzy paradigm, numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter test stand. Using a simple fast Fourier transform technique for feature extraction, the proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification.

Algorithms↗