Comparison of a discrete wavelet transform method and a modified undecimated discrete wavelet transform method for denoising of mammograms

Integration of stationary wavelet transform on a dynamic partial reconfiguration for recognition of pre-ictal gamma oscillations

To define the neural networks responsible of an epileptic seizure, it is useful to perform advanced signal processing techniques. In this context, electrophysiological signals present three types of waves: oscillations, spikes, and a mixture of both. Recent studies show that spikes and oscillations should be separated properly in order to define the accurate neural connectivity during the pre-ictal, seizure and inter-ictal states. Retrieving oscillatory activity is a sensitive task due to the frequency overlap between oscillations and transient activities. Advanced filtering techniques have been proposed to ensure a good separation between oscillations and spikes. It would be interesting to apply them in real time for instantaneous monitoring, seizure warning or neurofeedback systems. This requires improving execution time. This constraint can be overcome using embedded systems that combine hardware and software in an optimized architecture.

We propose here to implement a stationary wavelet transform (SWT) as an adaptive filtering technique retaining only pre-ictal gamma oscillations, as validated in previous work, on a partial dynamic configuration. Then, the same architecture is used with further modifications to integrate spatio temporal mapping for an early recognition of seizure build-up.

Data that contains transient, pre-ictal gamma oscillations and a seizure was simulated. the method on real intracerebral signals was also tested. The SWT was integrated on an embedded architecture. This architecture permits a spatio temporal mapping to detect the accurate time and localization of seizure build-up, while reducing computation time by a factor of around 40. Embedded systems are a promising venue for real-time applications in clinical systems for epilepsy.

Improving image quality in medical images using a combined method of undecimated wavelet transform and wavelet coefficient mapping

We propose a method for improving image quality in medical images by using a wavelet-based approach. The proposed method integrates two components: image denoising and image enhancement. In the first component, a modified undecimated discrete wavelet transform is used to eliminate the noise. In the second component, a wavelet coefficient mapping function is applied to enhance the contrast of denoised images obtained from the first component. This methodology can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. To confirm its superiority over existing state-of-the-art methods, the proposed method is experimentally evaluated via 30 mammograms and 20 chest radiographs. It is demonstrated that the proposed method can further improve the image quality of mammograms and chest radiographs, as compared to two other methods in the literature. These results reveal the effectiveness and superiority of the proposed method.