ABSTRACT
Objective To investigate a wavelet-transform-based approach that reduces the noise of medical images, and to compare the difference of the effects by different wavelet types. Methods A soft threshold approach based on the modification of local coefficient of wavelets was proposed. Firstly, a local modulus extrema distribution of the image, M j,m,n is obtained using wavelet transform. Then the modulus maximum was calculated and a threshold Tm was defined according to the statistical properties of the local modulus extrema distribution. If the extremum of the wavelet transform was greater than or equal to the threshold Tm, the corresponding wavelet coefficient was kept unchanged; while if the extremum of the wavelet transform was less than the threshold Tm, its corresponding wavelet coefficient was calculated using the soft threshold approach. Lastly, an inverse wavelet transform was performed according to the wavelet coefficients of these two parts so that the image could be reconstructed. Results The proposed approach could filter out the noise in medical images effectively, and the effects of noise reduction by different wavelets were different. Conclusion A useful wavelet threshold noise reduction algorithm can be obtained by wavelet multi-dimensional decomposition of image with proper selection of wavelet base function, and comparatively ideal effect of noise reduction can be achieved using this algorithm.
ABSTRACT
Feature extraction of event related potential (ERP) plays an important part in both fundamental and clinical researches for cerebral neurophysiology.Based on the prior knowledge of feature distribution of ERP,this paper introduces an approach to feature extraction of ERP from composite EEG,which is combined with wavelet multiresolution analysis (MRA) and radial basis function neural network (RBFNN).The components related to low-frequency response can be extracted from the wavelet decomposition coefficients by RBFNN.Then signal reconstruction is implemented to obtain the feature of ERP.Experimental result demonstrates that the approach is reliable.