Qianqing Qin

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 pre-segmentation 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.

A region-based segmentation method for ultrasound images in HIFU therapy

PURPOSE Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patients body. Therefore, the segmentation becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region-based segmentation method for uterine fibroids in HIFU guidance images is proposed. METHODS Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region-based split-and-merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel-based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first-order and second-order texture features computed from the gray levels and the gray level co-occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori information about the tumor position, shape, and size. Additionally, an appropriate cluster number for spectral clustering can be determined by the same algorithm, thus the automatic segmentation of the tumor region is achieved. RESULTS To evaluate the performance of the proposed method, 50 uterine fibroid ultrasound images from different patients receiving HIFU therapy were segmented, and the obtained tumor contours were compared with those delineated by an experienced radiologist. For area-based evaluation results, the mean values of the true positive ratio, the false positive ratio, and the similarity were 94.42%, 4.71%, and 90.21%, respectively, and the corresponding standard deviations were 2.54%, 3.12%, and 3.50%, respectively. For distance-based evaluation results, the mean values of the normalized Hausdorff distance and the normalized mean absolute distance were 4.93% and 0.90%, respectively, and the corresponding standard deviations were 2.22% and 0.34%, respectively. The running time of the segmentation process was 12.9 s for a 318 × 333 (pixels) image. CONCLUSIONS Experiments show that the proposed method can segment the tumor region accurately and efficiently with less manual intervention, which provides for the possibility of automatic segmentation and real-time guidance in HIFU therapy.

An efficient multi-scale waveform inversion method in Laplace-Fourier domain

Abstract Aiming at the problem that large computational resources and long computation time are required for the conventional Laplace-Fourier domain waveform inversion, an efficient multi-scale grid algorithm with variable computed area is proposed, and used in inversion modeling of the Marmousi and Overthrust model. This algorithm can choose a proper grid spacing automatically according to the different frequency, and adjust the depth of computing area according to the Laplace damping constant. This algorithm not only improves inversion efficiency significantly without the loss of inversion precision, but also improves the stability due to the decrease of grid number. Inversion results of the Marmousi and Overthrust model demonstrate the validity of the algorithm. In addition, the inversion results by the algorithm still can be approximate to the real model when low frequency information is missing.

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy.

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.

Distance estimation in ultrasound images using specific decorrelation curves

Speckle decorrelation algorithm is a method using decorrelation curves to estimate the distance between two neighboring 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 decorrelation 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 relationship also perform well in the distance estimation test.

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.

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 compression.

Segmentation of tumor ultrasound image in HIFU therapy based on texture and boundary encoding

It is crucial in high intensity focused ultrasound (HIFU) therapy to detect the tumor precisely with less manual intervention for enhancing the therapy efficiency. Ultrasound image segmentation becomes a difficult task due to signal attenuation, speckle effect and shadows. This paper presents an unsupervised approach based on texture and boundary encoding customized for ultrasound image segmentation in HIFU therapy. The approach oversegments the ultrasound image into some small regions, which are merged by using the principle of minimum description length (MDL) afterwards. Small regions belonging to the same tumor are clustered as they preserve similar texture features. The mergence is completed by obtaining the shortest coding length from encoding textures and boundaries of these regions in the clustering process. The tumor region is finally selected from merged regions by a proposed algorithm without manual interaction. The performance of the method is tested on 50 uterine fibroid ultrasound images from HIFU guiding transducers. The segmentations are compared with manual delineations to verify its feasibility. The quantitative evaluation with HIFU images shows that the mean true positive of the approach is 93.53%, the mean false positive is 4.06%, the mean similarity is 89.92%, the mean norm Hausdorff distance is 3.62% and the mean norm maximum average distance is 0.57%. The experiments validate that the proposed method can achieve favorable segmentation without manual initialization and effectively handle the poor quality of the ultrasound guidance image in HIFU therapy, which indicates that the approach is applicable in HIFU therapy.

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 frequency-domain 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.

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.