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Detection of left ventricle systolic dysfunction from shape deformity.

Coronary artery disease producing ischemic cardiomyopathy is the most frequent cause of left ventricular systolic dysfunction. Non-ischemic cardiomyopathies can also produce systolic dysfunction; they may be inherited as genetic disorders or occur sporadically. These coronary injuries have repercussions on the left ventricle producing changes on wall contractility, the shape of the cavity and also changes on ventricular function. This study is focused on the 2D echocardiograms of the left ventricle. Apical two chamber and four chamber view recordings were performed on normal and systolic dysfunction subjects. Individual frames were extracted for at least five cardiac cycles. After pre-processing these images, segmentation of the left ventricle was performed by Fuzzy systems. Then the volumes were measured by single and biplane methods along with the perimeter, short axis length and long axis length in each frame, from which the two indices Sphericity Index (SI) and Normalized Eccentricity Index (NEI) was determined. It was found that the diastolic phase is short in the case of systolic dysfunction, and its volume variation is not uniform as in the normal case. Also, in the case of systolic dysfunction, the span of either the long or short axis length variation is less than 0.5 cm. This depicts that akinesis is in the corresponding direction; the value of SI is less than 2 for systolic dysfunction. A sharp peak is seen at each systole point in the NEI plot and also its variation is smooth in subjects having LVEF > 45%, which is not the case for dysfunction.

Algorithms↗

Adaptable fuzzy C-Means for improved classification as a preprocessing procedure of brain parcellation.

Parcellation, one of several brain analysis methods, is a procedure popular for subdividing the regions identified by segmentation into smaller topographically defined units. The fuzzy clustering algorithm is mainly used to preprocess parcellation into several segmentation methods, because it is very appropriate for the characteristics of magnetic resonance imaging (MRI), such as partial volume effect and intensity inhomogeneity. However, some gray matter, such as basal ganglia and thalamus, may be misclassified into the white matter class using the conventional fuzzy C-Means (FCM) algorithm. Parcellation has been nearly achieved through manual drawing, but it is a tedious and time-consuming process. We propose improved classification using successive fuzzy clustering and implementing the parcellation module with the modified graphic user interface (GUI) for the convenience of users.

Algorithms↗

Pathways of paracetamol absorption from layered excipient suppositories: artificial intelligence approach.

When studying paracetamol availability after rectal administration, the differences between slower and faster release suppositories were discovered. Approach with modelling and simulation of compartment-based models was used to explore the differences. A study of paracetamol from layered excipient suppositories shows that many different mechanisms are involved in the drug pharmacokinetics. There is also a large number of articles, each dealing with only one or with a few of the mechanisms. However, there is little information available on how the mechanisms interact in the organism and thus govern the pharmacokinetics of the drug, which means that systemic view in the expert knowledge is missing. In the case of paracetamol rectal availability the use of partially fuzzyfied model allowed systemic combination of all described mechanisms found in the literature and measured data. In spite of non-identifiability, the model showed that patterns that explained differences in bioavailabilities of the two formulations of suppositories could be found. Results of modelling and simulation show that "in vivo" there is practically no difference in cumulative release profiles between the two formulations. However, due to higher content of mono-di-glycerides in a slower release formulation, the extent of absorption is augmented both by absorption-enhancing effect of mono-di-glycerides and the liver bypass mechanism via diminished viscosity.

Acetaminophen↗

Relative renal uptake and transit time measurements using functional factor images and fuzzy regions of interest.

The aim of the study was a quantitative comparison of relative renal uptake and both the whole-kidney and the parenchymal transit time derived from factor analysis of image sequences and provided by standard clinical procedues. In order to extract the stable, well-interpretable factors, factor analysis was performed locally in the problem-specific time and spatial windows and the resulting factor images either evaluated directly as functional images or used as fuzzy regions of interest (ROIs) for the subsequent extraction of time-activity curves from the analysed data. The values of relative renal uptake of the left kidney measured in the functional factor images, which demonstrate the initial accumulation of activity in renal parenchyma (mean 51.0%), did not differ significantly from the values obtained by a standard method (mean 51.5%, r = 0.98, P<0.001). Whole-kidney transit time calculated using fuzzy ROI curves correlated well with the reference values (r = 0.84, P<0.001); however, both its mean value (336.5 s) and the standard deviation (151.5 s) were substantially greater than those of the values provided by a standard procedure (262.8+/-86.9 s). Parenchymal transit time calculated using ROI curves correlated better with the transit time through a wider corticomedullary region rather than through a narrow cortical region, which is decisive in a differential diagnosis of renal disorders. In general, values of transit times provided by factor analysis correlated well with those provided by reference methods but with a shift towards the higher numerical values. This may have been a consequence of a greater extent of the automatically extracted fuzzy ROIs, or of occasionally delayed accumulation in the upper calyces. Results of the study provide quantitative evidence that the factor analysis of dynamic data, even without the introduction of prior physiological information, may yield clinically relevant information. However, some basic requirements, such as sufficiently high sampling frequency and count rate, adaption of the method to a specific clinical task, and proper selection of time and spatial windows for locally performed analysis, have to be fullfilled if the method is to be successfully applied clinically.

Algorithms↗

Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering.

The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D2/D3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [123I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r2>0.99, P<0.0001). Similarly, FCM gave segmentation of the [123I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r2=0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner.

Animals↗

A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS environment.

Appropriate land management decisions are important for current and future use of the land to ensure its sustainability. This requires that land management units (LMUs) be specified to enable the identification of specific parameters employed in decision making processes. This paper presents the development of a conceptual model, within geographic information systems (GIS), for defining and assessing LMUs from available biophysical information. The model consists of two main components (sub-models): land quality-based suitability analysis and soil erosion estimation. Using a fuzzy set methodology, the first sub-model was constructed to derive a land suitability index (LSI) for a cropping land utilization type. The LSI thus highlights the suitability grades of every pixel in the study area on a continuous basis. A submodel of soil erosion was established based on the Revised Universal Soil Loss Equation (RUSLE) utilising the same spatial data bases employed for structuring the LSI. Using a soil loss tolerance principle, a fuzzy membership function of average annual soil loss (called soil loss index, SLI) was established, leading to compatibility between LSI and SLI for data integration. LMUs were then derived from various combinations of LSI and SLI. The methodology developed shows the significance of the model for refining available land suitability evaluation systems, which take no account of expected land degradation (from erosion) due to a nominated land use. It also provides a valuable guideline for cost-effective GIS applications in the identification and assessment of homogeneous land units, using available spatial information sets, at a finer scale.

Conservation of Natural Resources↗

Fuzzy decision analysis for integrated environmental vulnerability assessment of the mid-Atlantic Region.

A fuzzy decision analysis method for integrating ecological indicators was developed. This was a combination of a fuzzy ranking method and the analytic hierarchy process (AHP). The method was capable of ranking ecosystems in terms of environmental conditions and suggesting cumulative impacts across a large region. Using data on land cover, population, roads, streams, air pollution, and topography of the Mid-Atlantic region, we were able to point out areas that were in relatively poor condition and/or vulnerable to future deterioration. The method offered an easy and comprehensive way to combine the strengths of fuzzy set theory and the AHP for ecological assessment. Furthermore, the suggested method can serve as a building block for the evaluation of environmental policies.

Air Pollution↗

Fuzzy multiple-criteria decision-making approach for industrial green engineering.

This paper describes a fuzzy hierarchical analytic approach to determine the weighting of subjective judgments. In addition, it presents a nonadditive fuzzy integral technique to evaluate a green engineering industry case as a fuzzy multicriteria decision-making (FMCDM) problem. When the investment strategies are evaluated from various aspects, such as economic effectiveness, technical feasibility, and environmental regulation, it can be regarded as an FMCDM problem. Since stakeholders cannot clearly estimate each considered criterion in terms of numerical values for the anticipated alternatives/strategies, fuzziness is considered to be applicable. Consequently, this paper uses triangular fuzzy numbers to establish weights and anticipated achievement values. By ranking fuzzy weights and fuzzy synthetic utility values, we can determine the relative importance of criteria and decide the best strategies. This paper applies what is called a lambda fuzzy measure and nonadditive fuzzy integral technique to evaluate the synthetic performance of green engineering strategies for aquatic products processors in Taiwan. In addition, we demonstrate that the nonadditive fuzzy integral is an effective evaluation and appears to be appropriate, especially when the criteria are not independent.

Conservation of Natural Resources↗

A participatory approach to ecosystem conservation: fuzzy cognitive maps and stakeholder group analysis in Uluabat Lake, Turkey.

Fuzzy cognitive mapping was used to develop a participatory ecosystem management plan for Uluabat Lake, Turkey. Interviews were conducted with stakeholders belonging to six different groups. Lake pollution was the most central and most mentioned variable for stakeholders. Stakeholder groups agree that lake pollution is negatively affecting ecosystem health and thereby local economies. Thus, reducing lake pollution was chosen as the overall goal for the management plan. Possible ways to reduce lake pollution and increase ecosystem health were seen differently by the different groups. Hunters, factory managers, NGO personnel, and local people thought industry was the main cause of lake pollution, while officials from the government and local municipalities thought roads and urban development contributed the most to lake pollution. Generally the stakeholder groups did not perceive their own actions as affecting the lake as strongly as other groups thought. For example, factory managers viewed factory pollution as negatively affecting the lake but less strongly than the other groups did. According to policy option simulations, reducing lake pollution had positive effects on all variables, especially fish, birds, animal husbandry, irrigation, agriculture, and the ecological balance of the lake. Results of this analysis were used to facilitate meetings among stakeholder groups and to develop a participatory ecosystem management plan. The analysis was useful for pointing out the similarities as well as the differences among the groups. It also helped the facilitators understand the focus of each stakeholder group and enabled them to suggest activities in which each group would want to participate.

Conservation of Natural Resources↗

Application of multiattribute decision-making methods for the determination of relative significance factor of impact categories.

A relative significance factor (f(i)) of an impact category is the external weight of the impact category. The objective of this study is to propose a systematic and easy-to-use method for the determination of f(i). Multiattribute decision-making (MADM) methods including the analytical hierarchy process (AHP), the rank-order centroid method, and the fuzzy method were evaluated for this purpose. The results and practical aspects of using the three methods are compared. Each method shows the same trend, with minor differences in the value of f(i). Thus, all three methods can be applied to the determination of f(i). The rank order centroid method reduces the number of pairwise comparisons by placing the alternatives in order, although it has inherent weakness over the fuzzy method in expressing the degree of vagueness associated with assigning weights to criteria and alternatives. The rank order centroid method is considered a practical method for the determination of f(i) because it is easier and simpler to use compared to the AHP and the fuzzy method.

Decision Making↗

Fuzzy modeling of interactions among environmental stressors in the ecosystem of Lake Koronia, Greece.

The development of a model for assessing the impact and interactions of stressors in the ecosystem of Lake Koronia, based on fuzzy inference, is presented in this paper. The proposed fuzzy inference model assesses the synergistic interactions among several significant stressors on fish production. These stressors include industrial pollution, pesticide and nutrient usage due to agricultural activities, and water level decrease due to irrigation works. Apart from the experts' knowledge, expressed in a set of fuzzy rules, a number of parameters such as pH, conductivity, biochemical and chemical oxygen demand, and nitrate concentration were used as stressor indicators. The proposed model is capable of simulating the effect of a large variety of environmental conditions, and it can be used as a dynamic tool for ecosystem risk assessment since it produces both qualitative and quantitative results, allowing for comparisons of predictions with on-going observational research and ecosystem monitoring. Its operation was successfully verified for a number of different conditions, ranging from low stressor impact to high stressor impact (where, in fact, the fish production was diminished). Moreover, the proposed fuzzy inference model can be used as a tool for the investigation of the behavior of the aquatic ecosystem under a large number of hypothetical environmental risk scenarios.

Animals↗

Land evaluation for maize based on fuzzy set and interpolation.

The objective of this article is to apply fuzzy set and interpolation techniques for land suitability evaluation for maize in Northern Ghana. Land suitability indices were computed at point observations using the Semantic Import (SI) model, whereas spatial interpolation was carried out by block kriging. Interpolated land suitability shows a high correlation (R2 = 0.87) with observed maize yield at the village level. This indicates that land suitability is closely related to maize yield in the study area. Membership functions were further used to assess the degree of limitation of land characteristics to maize. Sixty percent of the data has membership functions ranging from 0.23 for ECEC to 1.00 for drainage. ECEC, organic C, and clay are the major constraints to maize yield. The use of the fuzzy technique is helpful for land suitability evaluation, especially in applications in which subtle differences in soil quality are of a major interest. Furthermore, the use of kriging that exploits spatial variability of data is useful in producing continuous land suitability maps and in estimating uncertainties associated with predicted land suitability indices.

Agriculture↗

Sustainability at the local scale: defining highly aggregated indices for assessing environmental performance. The province of Reggio Emilia (Italy) as a case study.

In order to achieve improved sustainability, local authorities need to use tools that adequately describe and synthesize environmental information. This article illustrates a methodological approach that organizes a wide suite of environmental indicators into few aggregated indices, making use of correlation, principal component analysis, and fuzzy sets. Furthermore, a weighting system, which includes stakeholders' priorities and ambitions, is applied. As a case study, the described methodology is applied to the Reggio Emilia Province in Italy, by considering environmental information from 45 municipalities. Principal component analysis is used to condense an initial set of 19 indicators into 6 fundamental dimensions that highlight patterns of environmental conditions at the provincial scale. These dimensions are further aggregated in two indices of environmental performance through fuzzy sets. The simple form of these indices makes them particularly suitable for public communication, as they condensate a wide set of heterogeneous indicators. The main outcomes of the analysis and the potential applications of the method are discussed.

Cities↗

A hierarchical fuzzy decision support system for the environmental rehabilitation of Lake Koronia, Greece.

This article presents the design of a fuzzy decision support system (DSS) for the assessment of alternative strategies proposed for the restoration of Lake Koronia, Greece. Fuzzy estimates for the critical characteristics of the possible strategies, such as feasibility, environmental impact, implementation time, and costs are evaluated and supplied to the fuzzy DSS. Different weighting factors are assigned to the critical characteristics and the proposed strategies are ordered with respect to the system responses. The best strategies are selected and their expected impact on the ecosystem is evaluated with the aid of a fuzzy model of the lake. Sensitivity analysis and simulation results have shown that the proposed fuzzy DSS can serve as a valuable tool for the selection and evaluation of appropriate management actions.

Costs and Cost Analysis↗

Ecological risk assessment and problem formulation for Lake Uluabat, a Ramsar State in Turkey.

Unlike the United States and the European Union, developing countries do not have sufficiently structured legal and institutional systems to apply certain environmental management tools such as ecological risk assessment. However, it is important for countries with valuable environmental and ecological resources to have appropriate tools and to strengthen their environmental management capabilities and capacities for the sake of those resources. The case study described in this paper attempts to be a case study towards developing environmental management plans, especially in developing countries. The problem formulation step of Ecological Risk Assessment applied in this study contributed to the basic elements of an environmental management plan including the following: the partnership-building process, prioritization of the problems and issues of the ecosystem, and development of the action plan. Based on the information provided by participants from a series of workshops held to develop an environmental management plan for Uluabat Lake, ecosystem risks were ranked and an action plan was formed. The results obtained with the aid of fuzzy set theory provided a base for identification of the action steps by allowing scientific information to be included in the process. The degree to which Uluabat Lake's problem formulation fits into the existing legal framework of Turkey is also analyzed in this paper.

Conservation of Natural Resources↗

A human motor control perspective to multiple manipulator modelling.

This paper describes the aspects involved in modelling a multi-robot system from a human motor control perspective. The human motor control system has a hierarchical and decentralised structure, and building a control system for a multi-robot system that attains human features would require a decomposable model. Decomposition of a complex robotic system is difficult due to the interactions between the subsystems, so these have to be first separated before the system is modelled. The proposed method of separating the interconnections is applied with the aid of fuzzy modelling to derive a fully decomposable model of two manipulator robots handling a common object.

Fuzzy Logic↗

An adaptive neuro-fuzzy method (ANFIS) for estimating single-trial movement-related potentials.

This study aims to recover transient, trial-varying evoked potentials (EPs), in particular the movement-related potentials (MRPs), embedded within the background cerebral activity at very low signal-to-noise ratios (SNRs). A new adaptive neuro-fuzzy technique will attempt to estimate movement-related potentials within multi-channel EEG recordings, enabling this method to completely adapt to each input sweep without system training procedures. We assume that one of the sensors is corrupted by noise deriving from other sensors via an unknown function that will be estimated. We will approach this problem by: (1) spatially decorrelating the sensors in the preprocessing phase, (2) choosing the most informative of the filtered channels that will permit the best MRP estimation (input-selection phase) and (3) training the neuro-fuzzy model to fit the noise over the chosen sensor and therefore estimating the buried MRP. We tested this framework with simulations to validate the analytical results before applying them to the real biological data. Whenever it is applied to biological data, this method improves the SNR by more than 12 dB, even to very low SNRs. The processing method proposed here is likely to complement other estimation techniques and can be useful to process, enhance and analyse single-trial MRPs.

Adult↗

Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means.

The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI were acquired for segmentation analysis. Single-slice axial T2* fast low-angle shot (FLASH) images were obtained during the functional study. Pixelwise cross-correlation analysis was performed to obtain an activation map. A cascaded algorithm, combining Kohonen feature maps and fuzzy C means, was applied for segmentation. After processing, masks for gray matter, white matter, small vessels, and large vessels were generated. Tissue-specific analysis showed a signal change rate of 4.53% in gray matter, 2.98% in white matter, 5.79% in small vessels, and 7.24% in large vessels. Different temporal patterns as well as different levels of activation were identified in the functional response from various types of tissue. High correlation exists between cortical gray matter and subcortical white matter (r = 0.957), while the vessel behaves somewhat different temporally. The cortical gray matter fits best to the assumed input function (r = 0.957) followed by subcortical white matter (r = 0.829) and vessels (r = 0.726). The automated algorithm of tissue-specific analysis thus can assist functional MRI studies with different modalities of response in different brain regions.

Adult↗