De-noising of EEG Signals With Shift Based Cycle Spinning on Wave Atoms

To predict our brain disorders, EEG signals need to be analyzed. However, most EEG signals were affected by different kinds of noise during their acquision. So, signal analysis become most difficult due to the contamination of various noise. An appropriate technique is necessary to remove the noise from the signal. Wavelet Transform is one the most widely used technique for removing the noise from EEG signals. Wave atom is one of the new multiscale-multidirectional transforms, which is better than both wavelet as well as curvelet transforms. This wave atom transform has good orientation characteristic by which it preserves the edges in an efficient manner. This paper introduced a new method for denoising of EEG signal by shift-based cycle spinning on wave atom transform. Cycle spinning is a technique can be used to enhance the capability of wave atoms. An original EEG signals from public EEG database were used for this experiment. The results are analysed based on the performance measurements like SNR and MSE. The experimental results show that cycle spinning technique with appropriate shifts could be the better choice to denoise an EEG signals.

Ripplet-Transform-based Cycle Spinning Denoising and Fuzzy-CLA Segmentation of Retinal Images for Accurate Hard Exudates and Lesion Detection

Aims: Digital retinal images are commonly used for hard exudates and lesion detection. These images are rarely noiseless and therefore before any further processing they should be underwent noise removal. Background: An efficient segmentation method is then needed to detect and discern the lesions from the retinal area. Objective: In this paper, a hybrid method is presented for digital retinal image processing for diagnosis and screening purposes. The aim of this study is to present a supervised/semi-supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure. Methods: Ripplet transform and cycle spinning method is first used to remove the noises and artifacts. Results: The noises may be normal or any other commonly occurring forms such as salt and pepper. The image is transformed into fuzzy domain after it is denoised. Conclusion: A cellular learning automata model is used to detect any abnormality on the image which is related to a lesion. The automaton is created with an extra term as the rule updating term to improve the adaptability and efficiency of the cellular automata.Three main statistical criteria are introduced as the sensitivity, specificity and accuracy. A number of 50 retinal images with visually detection hard exudates and lesions are the experimental dataset for evaluation and validation of the method.

Reduced Cycle Spinning Method for the Undecimated Wavelet Transform

The Undecimated Wavelet Transform is commonly used for signal processing due to its advantages over other wavelet techniques, but it is limited for some applications because of its computational cost. One of the methods utilized for the implementation of the Undecimated Wavelet Transform is the one known as Cycle Spinning. This paper introduces an alternative Cycle Spinning implementation method that divides the computational cost by a factor close to 2. This work develops the mathematical background of the proposed method, shows the block diagrams for its implementation and validates the method by applying it to the denoising of ultrasonic signals. The evaluation of the denoising results shows that the new method produces similar denoising qualities than other Cycle Spinning implementations, with a reduced computational cost.