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PubMed2004

Local fuzzy fractal dimension and its application in medical image processing.

The local fuzzy fractal dimension (LFFD) is proposed to extract local fractal feature of medical images. The definition of LFFD is an extension of the pixel-covering method by incorporating the fuzzy set. Multi-feature edge detection is implemented with the LFFD and the Sobel operator. The LFFD can also serve as a characteristic of motion in medical image sequences. The experimental results show that the LFFD is an important feature of edge areas in medical images and can provide information for segmentation of echocardiogram image sequences.

Echocardiography↗
PubMed2004

Adaptive ventilator FiO2 advisor: use of non-invasive estimations of shunt.

A non-invasive and simple method of parameter estimation has been developed for the model-based decision support of the artificial ventilation in intensive care units. The parameter concerned was the respiratory shunt. Originally, the shunt had to be estimated using a numerical algorithm, which was slow and unreliable. The estimation process also required the knowledge of other parameters, whose values could only be obtained using invasive monitoring equipment. In this paper, the respiratory index is used for the shunt estimation. A linear regression model and a non-linear adaptive neuro-fuzzy inference system (ANFIS) model were used to describe the relationship between the respiratory index and the shunt. The shunts estimated using these models were then used to calculate the fractional inspired oxygen needed to attain the target arterial oxygen level of the model patient. The advisor also utilises population median values of the cardiac index and oxygen consumption index. This alleviates the need for invasive monitoring. In a simulation study, the mean squared error of the control using the ANFIS model was 0.75 kPa2 compared to 2.06 kPa2 using the linear regression model. Therefore, the performance of the FiO2 advisor was better when the shunt was estimated using the non-linear ANFIS model.

Algorithms↗
PubMed2004

Evolving rule-based systems in two medical domains using genetic programming.

OBJECTIVE: To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. MATERIAL: Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. METHODS: Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. RESULTS: Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. CONCLUSION: The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

Aphasia↗
PubMed2005

Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms. Part I. Classification of depth of anaesthesia and development of a patient model.

OBJECTIVE: The first part of this research relates to two strands: classification of depth of anaesthesia (DOA) and the modelling of patient's vital signs. METHODS AND MATERIAL: First, a fuzzy relational classifier was developed to classify a set of wavelet-extracted features from the auditory evoked potential (AEP) into different levels of DOA. Second, a hybrid patient model using Takagi-Sugeno Kang fuzzy models was developed. This model relates the heart rate, the systolic arterial pressure and the AEP features with the effect concentrations of the anaesthetic drug propofol and the analgesic drug remifentanil. The surgical stimulus effect was incorporated into the patient model using Mamdani fuzzy models. RESULTS: The result of this study is a comprehensive patient model which predicts the effects of the above two drugs on DOA while monitoring several vital patient's signs. CONCLUSION: This model will form the basis for the development of a multivariable closed-loop control algorithm which administers "optimally" the above two drugs simultaneously in the operating theatre during surgery.

Algorithms↗
PubMed2005

Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms Part II. Closed-loop control of simultaneous administration of propofol and remifentanil.

OBJECTIVE: Part II of this research study is concerned with the development of a closed-loop simulation linking the patient model as well as the fuzzy relational classifier already introduced in Part I with a control algorithm. The overall architecture is in fact a system advisor, which provides information to the anaesthetist about the adequate infusion-rates of propofol and remifentanil simultaneously. METHODS AND MATERIAL: The developed fuzzy multivariable controller includes three rule-bases and takes into account the synergetic interactions between the above drugs and uses such knowledge to achieve rapidly the desired depth of anaesthesia (DOA) level. RESULTS: The result of the study is a closed-loop control scheme, which adjusts efficiently the infusion-rates of two drugs in response to DOA changes. This controller can either be used in an advisory mode or closed-loop feedback mode in the operating theatre during surgery. CONCLUSION: It is hoped that this control scheme coupled with the patient model presented in Part I of this study will be used routinely in the operating theatre in the very near future.

Algorithms↗
PubMedDate not supplied

Fuzzy attributes of a DNA complex: development of a fuzzy inference engine for codon-"junk" codon delineation.

OBJECTIVE: The present study is concerned with the need that exists in bioinformatics to identify and delineate overlapping codon and noncodon structures in a deoxyribonucleic acid (DNA) complex so as to ascertain the boundary of separation between them. Codons refer to those parts in a DNA complex encoded towards forming a desired set of proteins. Also coexist in the DNA structure noncodons (or "junk" codons), whose functions are not so well defined. Such codon and noncodon parts (at least over some sections of a DNA chain) may conform to diffused (overlapping) states exhibiting sharpless boundaries with indistinctive statistics of occurrence of their constituents. Such overlapping mix of codon and noncodon entities constitutes a (fuzzy) universe with information constituent having a fuzzy structure, which can only be identified in descriptive norms with characteristic membership of belonging to certain attributes. Hence, this work is directed to develop a fuzzy inference engine (FIE), which delineates the fuzzy codon-noncodon parts. METHODS AND MATERIAL: Relevant algorithms developed for the fuzzy inference in question are based on information-theoretic (IT) considerations applied to symbolic as well as binary sequence data representing the DNA. Pseudocodes, as needed are furnished. RESULTS: Simulated studies using human and other bacterial codon statistics are presented to illustrate the efficacy of the approach pursued. The outcome of the study is illustrated via tabulated results and graphs depicting the delineation sought. CONCLUSION: The results signify the success of IT-approach pursued in delineating imprecise codon/noncodon boundaries. The FIE applies both for human and bacterial codon statistics.

Algorithms↗
PubMed2005

Advanced soft computing diagnosis method for tumour grading.

OBJECTIVE: To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied. MATERIAL AND METHODS: One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM. RESULTS: The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively. CONCLUSIONS: An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.

Algorithms↗
PubMed2006

Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains.

OBJECTIVE: The aim of this work is to provide a theoretical framework which is sufficiently expressive to describe temporal evolution of diseases, and also to propose a diagnostic process for building explanations of patient's observed temporal evolution based on these disease descriptions. BACKGROUND: Model-based diagnosis (MBD) tackles the problem of troubleshooting systems by starting from a description of their structure and function (or behaviour). It is in this area where the use of deep causal models, as part of MBD systems, has shown its greater efficiency over classical rule based systems. From its beginnings, the temporal dimension was considered as an important component in MBD, since it makes it possible to define the dynamic behaviour. Several approaches have been proposed to represent time in MBD, enabling the representation of temporal concepts and relations, as well as the use of temporal reasoning mechanisms. METHODOLOGY: We first propose a temporal behavioural model (TBM), which allows us to capture the dynamics underlying temporal evolution of diseases and to include contextual information. Contextual information is required to model how contextual factors change the temporal evolution of diseases. The temporal component is modelled by fuzzy temporal constraints networks (FTCN), which makes the representation of quantitative and qualitative imprecise temporal information possible. We also provide a diagnostic process, which is based on a temporal adaptation of classical cover and differentiate method. RESULTS: The TBM and diagnostic process proposed provides a unique framework which addresses three problems not dealt with together so far: (a) the inclusion of contextual information, (b) the expressivity of the solution provided, and (c) the evaluation of the diagnostic hypotheses. This proposal demonstrates that the FTCN formalism provides mechanisms sufficiently expressive to cope with the intrinsic imprecision in the description of diseases' temporal evolution. The explanation generated provides the user with a complete picture of the temporal evolution of diseases and its causal links, thus allowing the appearance of repeated instances of the same disease through time. Mechanisms are provided which evaluate the credibility of alternative hypotheses, based on possibility theory. A prototype is presented along with a knowledge acquisition tool that guides medical experts in the model building process. CONCLUSIONS: In this paper, we propose a model that tightly couples methods from MBD area with constraint-based temporal reasoning techniques. The proposed model allows us to model complex contextual relationships in a compact way as well as providing solutions expressive enough to be used for decision support purposes. The solution provided conforms a causal network entailing the abnormal observations, including pathophysiological and etiological states. Furthermore, different instances of the same diagnostic hypotheses, located at different time instants, are also possible in the final solution. Finally, we provide an analysis of related and future works.

Artificial Intelligence↗
PubMed2006

From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis.

OBJECTIVE: This article delineates a relatively unknown path in the history of medical philosophy and medical diagnosis. It is concerned with the phenomenon of vagueness in the physician's "style of thinking" and with the use of fuzzy sets, systems, and relations with a view to create a model of such reasoning when physicians make a diagnosis. It represents specific features of medical ways of thinking that were mentioned by the Polish physician and philosopher Ludwik Fleck in 1926. The paper links Lotfi Zadeh's work on system theory before the age of fuzzy sets with system-theory concepts in medical philosophy that were introduced by the philosopher Mario Bunge, and with the fuzzy-theoretical analysis of the notions of health, illness, and disease by the Iranian-German physician and philosopher Kazem Sadegh-Zadeh. MATERIAL: Some proposals to apply fuzzy sets in medicine were based on a suggestion made by Zadeh: symptoms and diseases are fuzzy in nature and fuzzy sets are feasible to represent these entity classes of medical knowledge. Yet other attempts to use fuzzy sets in medicine were self-contained. The use of this approach contributed to medical decision-making and the development of computer-assisted diagnosis in medicine. CONCLUSION: With regard to medical philosophy, decision-making, and diagnosis; the framework of fuzzy sets, systems, and relations is very useful to deal with the absence of sharp boundaries of the sets of symptoms, diagnoses, and phenomena of diseases. The foundations of reasoning and computer assistance in medicine were the result of a rapid accumulation of data from medical research. This explosion of knowledge in medicine gave rise to the speculation that computers could be used for the medical diagnosis. Medicine became, to a certain extent, a quantitative science. In the second half of the 20th century medical knowledge started to be stored in computer systems. To assist physicians in medical decision-making and patient care, medical expert systems using the theory of fuzzy sets and relations (such as the Viennese "fuzzy version" of the Computer-Assisted Diagnostic System, CADIAG, which was developed at the end of the 1970s) were constructed. The development of fuzzy relations in medicine and their application in computer-assisted diagnosis show that this fuzzy approach is a framework to deal with the "fuzzy mode of thinking" in medicine.

Diagnosis↗
PubMed2006

Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence.

OBJECTIVE: The paper aims at improving the prediction of superficial bladder recurrence. To this end, feedforward neural networks (FNNs) and a feature selection method based on unsupervised clustering, were employed. MATERIAL AND METHODS: A retrospective prognostic study of 127 patients diagnosed with superficial urinary bladder cancer was performed. Images from biopsies were digitized and cell nuclei features were extracted. To design FNN classifiers, different training methods and architectures were investigated. The unsupervised k-windows (UKW) and the fuzzy c-means clustering algorithms were applied on the feature set to identify the most informative feature subsets. RESULTS: UKW managed to reduce the dimensionality of the feature space significantly, and yielded prediction rates 87.95% and 91.41%, for non-recurrent and recurrent cases, respectively. The prediction rates achieved with the reduced feature set were marginally lower compared to the ones attained with the complete feature set. The training algorithm that exhibited the best performance in all cases was the adaptive on-line backpropagation algorithm. CONCLUSIONS: FNNs can contribute to the accurate prognosis of bladder cancer recurrence. The proposed feature selection method can remove redundant information without a significant loss in predictive accuracy, and thereby render the prognostic model less complex, more robust, and hence suitable for clinical use.

Algorithms↗
PubMed2006

A closed-loop hybrid physiological model relating to subjects under physical stress.

OBJECTIVE: The objective of this research study is to derive a comprehensive physiological model relating to subjects under physical stress conditions. The model should describe the behaviour of the cardiovascular system, respiratory system, thermoregulation and brain activity in response to physical workload. METHODS AND MATERIAL: An experimental testing rig was built which consists of recumbent high performance bicycle for inducing the physical load and a data acquisition system comprising monitors and PCs. The signals acquired and used within this study are the blood pressure, heart rate, respiration, body temperature, and EEG signals. The proposed model is based on a grey-box based modelling approach which was used because of the sufficient level of details it provides. Cardiovascular and EEG Data relating to 16 healthy subject volunteers (data from 12 subjects were used for training/validation and the data from 4 subjects were used for model testing) were collected using the Finapres and the ProComp+ monitors. For model validation, residual analysis via the computing of the confidence intervals as well as related histograms was performed. RESULTS: Closed-loop simulations for different subjects showed that the model can provide reliable predictions for heart rate, blood pressure, body temperature, respiration, and the EEG signals. These findings were also reinforced by the residual analyses data obtained, which suggested that the residuals were within the 90% confidence bands and that the corresponding histograms were of a normal distribution. A higher intelligent level was added to the model, based on neural networks, to extend the capabilities of the model to predict over a wide range of subjects dynamics. CONCLUSION: The elicited physiological model describing the effect of physiological stress on several physiological variables can be used to predict performance breakdown of operators in critical environments. Such a model architecture lends itself naturally to exploitation via feedback control in a 'reverse-engineering' fashion to control stress via the specification of a safe operating range for the psycho-physiological variables.

Cardiovascular System↗
PubMed2006

Advanced fuzzy cellular neural network: application to CT liver images.

OBJECTIVE: To achieve better boundary integrities and recall accuracies for segmented liver images, use of the advanced fuzzy cellular neural network (AFCNN), as a variant of the fuzzy cellular neural network (FCNN), is proposed to effectively segment CT liver images. MATERIALS AND METHODS: In order to better utilize relevant contour and gray information from liver images, we have improved the FCNN [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], which proved to be very effective for the segmentation of microscopic white blood cell images, to create the novel neural network, AFCNN. Its convergent property and global stability are proved. Based on the FCNN-based NDA algorithm [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], we developed the AFCNN-based NDA algorithm, which we used to segment 5 CT liver images. For comparison, we also segmented the same 5 CT liver images using the FCNN-based NDA algorithm. RESULTS AND CONCLUSION: : AFCNN has distinct advantages over FCNN in both boundary integrity and recall accuracy. In particular, the performance index Binary_rate is generally much higher for AFCNN than for FCNN when applied to CT liver images.

Fuzzy Logic↗
PubMed2005

Using supervised fuzzy clustering to predict protein structural classes.

Prediction of protein classification is both an important and a tempting topic in protein science. This is because of not only that the knowledge thus obtained can provide useful information about the overall structure of a query protein, but also that the practice itself can technically stimulate the development of novel predictors that may be straightforwardly applied to many other relevant areas. In this paper, a novel approach, the so-called "supervised fuzzy clustering approach" is introduced that is featured by utilizing the class label information during the training process. Based on such an approach, a set of "if-then" fuzzy rules for predicting the protein structural classes are extracted from a training dataset. It has been demonstrated through two different working datasets that the overall success prediction rates obtained by the supervised fuzzy clustering approach are all higher than those by the unsupervised fuzzy c-means introduced by the previous investigators [C.T. Zhang, K.C. Chou, G.M. Maggiora. Protein Eng. (1995) 8, 425-435]. It is anticipated that the current predictor may play an important complementary role to other existing predictors in this area to further strengthen the power in predicting the structural classes of proteins and their other characteristic attributes.

Algorithms↗
PubMed2005

Unsupervised fuzzy clustering analysis supports behavioral cutoff criteria in an animal model of posttraumatic stress disorder.

BACKGROUND: Unsupervised fuzzy clustering (UFC) analysis is a mathematical technique that groups together objects in the multidimensional feature space according to a specified similarity measurement, thereby yielding clusters of similar data points that can be represented by a set of prototypes or centroids. METHODS: Since clinical studies of mental disorders distinguish between affected and unaffected individuals, we designed an inclusion/exclusion criteria (cutoff behavioral criteria [CBC]) approach for animal behavioral studies. The effect of classifying the study population into clearly affected versus clearly unaffected individuals according to behaviors on two behavioral paradigms was statistically significant. RESULTS: Here the raw data from previous studies were subjected to UFC algorithms as a means of objectively testing the validity of the concept of the CBC for our experimental model. The first UFC algorithm yielded two clearly discrete clusters, found to consist almost exclusively of the exposed animals in the one and unexposed animals in the other. The second algorithm yielded three clusters corresponding to animals designated as clearly affected, partially affected, and clearly unaffected. The algorithm for physiological data in addition to behavioral data failed to elicit discrete clusters. CONCLUSIONS: The UFC analysis yielded data that support the conceptual contention of the CBC and lends additional validity to our previous behavioral studies.

Acoustic Stimulation↗
PubMed2006

Automatic control of biomass gasifiers using fuzzy inference systems.

A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.

Algorithms↗
PubMed2006

Comparison of polynomial and neural fuzzy models as applied to the ethanolamine pulping of vine shoots.

The influence of operational variables in the pulping of vine shoots by use of ethanolamine [viz. temperature (155-185 degrees C), cooking time (30-90min) and ethanolamine concentration (50-70% v/v)] on the properties of the resulting pulp (viz. yield, kappa index, viscosity and drainability) was studied. A central composite factorial design was used in conjunction with the software BMDP and ANFIS Edit Matlab 6.5 to develop polynomial and fuzzy neural models that reproduced the experimental results of the dependent variables with errors less than 10%. Both types of models are therefore effective with a view to simulating the ethanolamine pulping process. Based on the proposed equations, the best choice is to use values of the operational valuables resulting in near-optimal pulp properties while saving energy and immobilized capital on industrial facilities by using lower temperatures and shorter processing times. One combination leading to near-optimal properties with reduced costs is using a temperature of 180 degrees C and an ethanolamine concentration of 60% for 60min, to obtain pulp with a viscosity of 6.13% lower than the maximum value (932.8ml/g) and a drainability of 5.49% lower than the maximum value (71 (o)SR).

Conservation of Natural Resources↗
PubMed2006

A fuzzy expert system design for analysis of body sounds and design of an unique electronic stethoscope (development of HILSA kit).

In this paper we have developed a fuzzy expert system (FES) for different sounds produced by different organs in the human body. We have also constructed a unique electronic stethoscope. The human body sounds produced by different organs like heart, lungs and intestine were analyzed. The doctor provided the data and relation between variables chosen for each organ sound. Using this information a rule base for fuzzy expert system was built. Such FES helps the medical doctor in arriving at appropriate decision in different difficult clinical situations. The examination of body sounds was done using conventional stethoscope (CS) and electronic stethoscope (ES), which was uniquely designed for this study. We have found that unique stethoscope developed by us is far superior to conventional stethoscope by its overall performance.

Algorithms↗
PubMed2005

On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems.

Extracting comprehensible and general classifiers from data in the form of rule systems is an important task in many problem domains. This study investigates the utility of a multi-objective evolutionary algorithm (MOEA) for this task. Multi-objective evolutionary algorithms are capable of finding several trade-off solutions between different objectives in a single run. In the context of the present study, the objectives to be optimised are the complexity of the rule systems, and their fit to the data. Complex rule systems are required to fit the data well. However, overly complex rule systems often generalise poorly on new data. In addition they tend to be incomprehensible. It is, therefore, important to obtain trade-off solutions that achieve the best possible fit to the data with the lowest possible complexity. The rule systems produced by the proposed multi-objective evolutionary algorithm are compared with those produced by several other existing approaches for a number of benchmark datasets. It is shown that the algorithm produces less complex classifiers that perform well on unseen data.

Algorithms↗