ABSTRACT
Object High overlap of data window is essential to improve axial resolution in elastogaphy.However, correlated errors in displacement estimates increase dramatically with the increase of the overlap, and generate the so-called "worm" artifacts. This paper presents a wavelet shrinkage de-noising in strain estimates to reduce the worm artifacts at high overlap. Methods Each of axial strain A-lines was decomposed using discrete wavelet transformation up to 3 levels. The high frequency components of every levels of wavelet coefficients were quantified by using soft threshold function according to different adaptive thresholds. Then the discrete wavelet reconstruction were performed to produce a wavelet shrinkage denoised strain line. Results The simulation results illustrated that the presented technique could efficiently denoise worm artifacts and enhance the elastogram performance indices such as elastographic SNRe and CNRe. Elastogram obtained by wavelet denoising had the closest correspondence with ideal strain image. In addition, the results also demonstrated that wavelet shrinkage de-noising applied in strain estimates could obtain better image quality parameters than that apphed in displacement estimates. The elastic phantom experiments also showed the similar elastogram performance improvement. Conclusion Wavelet shrinkage de-noising can efficiently denoise the worm artifacts noise of elastogram and improve the performance indices of elastogram while maintaining the high axial resolution.
ABSTRACT
Objective:To provide the methodology reference for the gene expression data clustering.Methods:This paper proposed a fuzzy clustering ensemble algorithm based on wavelet de-noising.The new method is applied to the yeast cell's gene expression data,and the clustering results were compared with fuzzy ensemble clustering methods.Results:Apply micro-precision to evaluate the clustering results.The research indicated that the proposed method was superior to fuzzy ensemble clustering algotuthm.Conclusion:The proposed method has better clustering for the gene expression data clustering.
ABSTRACT
Objective To study on wavelet transform in calcium sparks image de-noising when excitation contraction cou-pling(ECC) was studied and researched by scanning confocal microscopy,in order to elucidate the mechanism of ECC.But due to the noise exist in most calcium sparks image,it is hardly to recognize and analyze the useful information of calcium sparks.So how to increase the signal-to-noise rate is very important in calcium spark studying.Methods By using the self-developed software based on multi-scale wavelet transform to remove the noise exists in the calcium sparks images.Results The experiments shows that the method can be increased the signal-to-noise rate in about 80 calcium sparks images.Con-clusion The new method for calcium spark expression and spatial and temporal analysis is provided.