Nonlinear Adaptive Wavelet Transform for Lossless Image Compression

The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear morphological predictor follows the update adaptive lifting to result in fewer large wavelet coefficients near edges for reducing coding. The nonlinear adaptive wavelet transforms can also allow perfect reconstruction without any overhead cost. Experiment results are given to show lower entropy of the adaptive transformed images than those of the non-adaptive case and great applicable potentiality in lossless image compresslon.

Segmentation of Tumor Ultrasound Image via Region-Based Ncut Method

To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for presegmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.

Absolute Ruin Problems for the Risk Processes with Interest and a Constant Dividend Barrier

In this paper, the absolute ruin in the compound Poisson risk model with interest and a constant dividend barrier is investigated. First, integro-differential equations satisfied by the expected discounted dividend payments are derived. The explicit expressions are obtained when the individual claim size is exponential distributed. Second, the moment generating function of the discounted dividends is considered, and integro-differential equations satisfied by the moment generating function of the discounted dividends are derived. Third, by a ＂differential＂ argument, the time to recovery to zero from a given negative surplus is considered. Finally, how long it takes for the surplus process to reach the dividend barrier is discussed.

Distance Estimation in Ultrasound Images Using Specific Decorrelation Curves

Speckle decorrelation algorithm is a method using decorrelation curves to estimate the distance between two neighbor- ing ultrasound images. In this paper, we propose a new method to obtain specific decorrelation curves for distance estimation. First, several datasets of synthetic ultrasound （US） images are obtained by scanning different scatters. Second, based on the US datasets, we compute low-order moments and the elevational decorrelation curves. Finally, low-order moments are used to classify different scattering conditions. The suitable decorrelation curves can be acquired when the scattering style has been determined. With these steps, the relationship between low order moments and the decor- relation curves is established by the scattering conditions. This relationship proves to be efficient and applicable in the experiment section. The decorrelation curves chosen according to the rela- tionship also perform well in the distance estimation test.

An Improved 3-D Ray Tracing Method Using Linear Traveltime Interpolation

An approach of three-dimensional seismic ray tracing is presented,which is derived from adopting two-dimensional linear traveltime interpolation（LTI）.By adjusting the forward process using the partition of grid interface,and backward step by considering more directions,the new approach is suitable for the application of three-dimensional models.The calculation results show that,with the same accuracy,the improved 3-D method is much faster than the method of traditional LTI directly applied in the three-dimensional case.

Application of Frequency-Domain Waveform Inversion Method in Marmousi Shots Data

Frequency-domain waveform seismic tomography includes modeling of wave propagation and full waveform inversion of correcting the initial velocity model. In the forward modeling, we use direct solution based on sparse matrix factorization, combined with nine-point finite-difference for the linear system of equations. In the waveform inversion, we use preconditioned gradient method where the preconditioner is provided by the diagonal of the approximate Hessian matrix. We successfully applied waveform inversion method from low to high frequency in two sets of Marmousi data. One is the data set generated by frequencydomain finite-difference modeling, and the other is the original Marmousi shots data set. The former result is very close to the true velocity model. In the original shots data set inversion, we replace the prior source with estimated source; the result is also acceptable, and consistent with the true model.