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In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability(More)
We used a robotic device to test the idea that impedance control involves a process of learning or adaptation that is acquired over time and permits the voluntary control of the pattern of stiffness at the hand. The tests were conducted in statics. Subjects were trained over the course of 3 successive days to resist the effects of one of three different(More)
It is very important to detect stages of multiple sclerosis (MS) lesions in order to exactly quantify involved voxels. In this paper, a novel method is proposed for automatic detection of different stages of MS lesions in the brain magnetic resonance (MR) images, in fluid attenuated inversion recovery (FLAIR) studies. In the proposed method, firstly, MS(More)
It is known that humans can modify the impedance of the musculoskeletal periphery, but the extent of this modification is uncertain. Previous studies on impedance control under static conditions indicate a limited ability to modify impedance, whereas studies of impedance control during reaching in unstable environments suggest a greater range of impedance(More)
Signature verification techniques utilize many different characteristics of an individual. The selection of signature features is critical in determining the performance of a signature verification system. Even though it is critical to select a suitable set of features to be extracted, emphasis has to be put into selecting an appropriate classifier for the(More)
In this study, a decision support system was designed to distinguish children with ADHD from other similar children behavioral disorders such as depression, anxiety, comorbid depression and anxiety and conduct disorder based on the signs and symptoms. Accuracy of classifying with Radial basis function and multilayer neural networks were compared. Finally,(More)
—Electrocardiogram (ECG) segmentation is necessary to help reduce the time consuming task of manually annotating ECG's. Several algorithms have been developed to segment the ECG automatically. We first review several of such methods, and then present a new single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and(More)
In a voluntary movement, the nervous system specifies not only the motor commands but also the gains associated with reaction to sensory feedback. For example, suppose that, during reaching, a perturbation tends to push the hand to the left. With practice, the brain not only learns to produce commands that predictively compensate for the perturbation but(More)
Science of human identification using physiological characteristics or biometry has been of great concern in security systems. However, robust multimodal identification systems based on audio-visual information has not been thoroughly investigated yet. Therefore, the aim of this work to propose a model-based feature extraction method which employs(More)
In this research, we evaluated the inter-relation of tremor and rigidity in Parkinson's disease. We included the agonist and antagonist skeletal muscle models as well as the peripheral (spinal and long-loop reflexes) and central (basal ganglia and cortex) mechanisms and present a complete model. All of our simulation is developed in SIMULINK, MATLAB 7. Our(More)