A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear.

Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper presents a type-2 fuzzy expert system for meniscal tear diagnosis using PD magnetic resonance images (MRI). The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. λ-nhancement algorithm is used to perform the pre-processing step. For the segmentation step, first, Interval Type-2 Fuzzy C-Means (IT2FCM) is applied to the images, outputs of which are then employed by Interval Type-2 Possibilistic C-Means (IT2PCM) to perform post-processes. Second stage concludes with re-estimation of "η" value to enhance IT2PCM. Finally, a Perceptron neural network with two hidden layers is used for Classification stage. The results of the proposed type-2 expert system have been compared with a well-known segmentation algorithm, approving the superiority of the proposed system in meniscal tear recognition.

A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors.

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.

Normal forms of fuzzy middle and fuzzy contradictions.

The expressions of "excluded middle" and "crisp contradiction" are reexamined starting with their original linguistic expressions which are first restated in propositional and then predicate forms. It is shown that, in order to generalize the truth tables and hence the normal forms, the membership assignments in predicate expressions must be separated from their truth qualification. In two-valued logic, there is no need to separate them from each other due to reductionist Aristotalean dichotomy. Whereas, in infinite (fuzzy) valued set and logic, the separation of membership assignments from their truth qualification forms the bases of a new reconstruction of the truth tables. The results obtained from these extended truth tables are reducible to their Boolean equivalents under the axioms of Boolean theory. Whereas, in fuzzy set and logic theory, we obtain a richer and more complex interpretations of the "fuzzy middle" and "fuzzy contradiction."

Using fuzzy logic to predict response to citalopram in alcohol dependence.

INTRODUCTION: The prediction of patient response to new pharmacotherapies for alcohol dependence has usually not been successful with standard statistical techniques. We hypothesized that fuzzy logic, a qualitative computational approach, could predict response to 40 mg/day citalopram and 40 mg/day citalopram with a brief psychosocial intervention in alcohol-dependent patients. METHODS: Two data sets were formed with patients from our studies who received 40 mg/day citalopram alone (n = 34) or 40 mg/day citalopram and a brief psychosocial intervention (n = 28). The output variable, "response," was the percentage decrease in alcohol intake from baseline. Input variables included age, gender, baseline alcohol intake, and levels of anxiety, depression, alcohol dependence, and alcohol-related problems. RESULTS: A fuzzy rulebase was created from the data of 26 randomly chosen patients who received 40 mg/day citalopram and was used to predict the responses of the remaining eight patients. Eight rules related response with depression, anxiety, alcohol dependence, alcohol-related problems, age, and baseline alcohol intake. The average magnitude of the error in the predictions (RMSE) was 2.6 with a bias (ME) of 0.6. Predicted and actual response correlated (r = 0.99; p < 0.001). A fuzzy rulebase was created from the data of 28 randomly chosen patients who received 40 mg/day citalopram and a brief psychosocial intervention and was used to predict the responses of the remaining five patients. Six rules related response with age, anxiety, depression, alcohol dependence, and baseline alcohol intake with good predictive performance (RMSE = 6.4; ME = -1.5; r = 0.96; p < 0.01). CONCLUSIONS: This study indicates that fuzzy logic modeling can predict response to pharmacotherapies for alcohol dependence.

Fuzzy logic pharmacokinetic modeling: application to lithium concentration prediction.

INTRODUCTION: We hypothesized that fuzzy logic could be used for pharmacokinetic modeling. Our objectives were to develop and evaluate a model for predicting serum lithium concentrations with fuzzy logic. METHODS: Steady-state pharmacokinetic data had been previously collected in 10 elderly patients (age range, 67 to 80 years) with depression who were receiving lithium once daily. Each patient had serial serum lithium concentration determinations over one 24-hour period. The resulting 137 data sets initially consisted of five input variables (age, weight, serum creatinine, lithium dose, and time since last dose) and one output variable (serum lithium concentration; range, 0.2 to 1.24 mmol/L). RESULTS: A fuzzy rulebase was created with 87 randomly chosen data sets, and predictions of serum lithium concentration were made on the basis of the remaining 50 data sets. All of the input variables except age and weight were identified as contributing to the fuzzy logic model. The average magnitude of the error in the predictions was 0.13 mmol/L (root mean squared error) with a bias (mean of the prediction errors) of 0.03 mmol/L. CONCLUSIONS: This study indicates that the use of fuzzy logic for pharmacokinetic modeling of lithium for serum concentration predictions is feasible.

Stabilization of unstable and unintuitive plants by fuzzy control.

A heuristically derived stabilization strategy for an unstable and unintuitive plant by fuzzy control is described. It is shown that the often used classical fuzzy controller, which is both static and time invariant, is incapable of stabilizing such types of plants. However, a simple modification to the classical fuzzy controller architecture that separates the measurement and control phases, together with a hierarchical control strategy, enable the unstable and unintuitive plant to be stabilized. The fuzzy control strategy, as well as the new fuzzy controller architecture, are based on the consideration of "what a human subject would do when dealing with a physical plant which is both unstable and unintuitive". The stabilization strategy is then generalized to other mathematically similar systems. While the rules for the stabilization of the plant are heuristically defined, the membership functions associated with the rules are tuned by a simulated annealing procedure.