Majlesi Journal of Multimedia Processing
Volume:2 Issue: 2, Jun 2013

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. Wavelet transform is particularly effective for representing various aspects of nonstationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective.Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In this study, we used DWT to decompose EEG signals into sub-bands and then line length and standard deviation features were extracted to combine with different Artificial Neural Networks (ANNs) to classify the EEG signals regarding the existence of seizure or not.

In this paper, a new video moving object tracking method is proposed. In initialization, a moving object selected by the user is segmented and the dominant color is extracted from the segmented target. In tracking step, a motion model is constructed to set the system model of adaptive Kalman filter firstly. Then, the dominant color of the moving object in HSI color space will be used as feature to detect the moving object in the consecutive video frames. The detected result is fed back as the measurement of adaptive Kalman filter and the estimate parameters of adaptive Kalman filter are adjusted by occlusion ratio adaptively. The proposed method has the robust ability to track the moving object in the consecutive frames under some kinds of real-world complex situations such as the moving object disappearing totally or partially due to occlusion by other ones, fast moving object, changing lighting, changing the direction and orientation of the moving object, and changing the velocity of moving object suddenly. The proposed method is an efficient video object tracking algorithm.

MS is one of the most common neurological diseases which infected more than 50000 people in Iran. Brain MRI is one of the diagnostic methods of MS. Brain MS lesions are only seen in white matter of the brain, so by segmentation of the brain white matter from other brain components, the speed and accuracy of the MS lesion detection will improve simultaneously. Unfortunately, because of the human errors, environmental condition, imaging equipments and etc., the MR imaging has lots of noise which will cause significant errors in segmentation of the MS lesions. Atlas base methods for white matter segmentation have the least sensitivity to noise and environmental condition, but it isn’t accurate enough. In this paper a new method based on thresholding and using smoothing filter is proposed to improve the performance of the atlas base methods in brain white matter segmentation. The comparison between doctor manual segmentation and the results of the proposed method proofs the performance of the proposed method in segmentation of the brain white matter especially in noisy images.