基于边缘定向扩散的图像增强方法

针对前向后向扩散方程不能较好的保持流线状结构,而相干增强不能锐化边缘的缺点, 提出一种新的基于边缘定向的张量型前向后向扩散模型.该模型将前向-后向扩散方程引进到张量型扩散方程中,在扩散系数的选取上合并了相干增强扩散与前向-后向扩散的长处,又克服了他们各自的缺点.采用类似相干增强扩散的边缘定向算子实现对边缘的定向,然后根据边缘定向的结果设置扩散张量的特征根,使扩散张量沿边缘方向为正向扩散以增强边缘,而垂直于边缘方向为逆扩散以锐化边缘.理论分析和数值计算表明,该方法具有比相干增强扩散及前向-后向扩散更好的增强效果.     

超高斯光束在超短脉冲激光系统中的传输

 为了增大光束的填充因子及提高放大器的能量转换效率,设计了一种新型的空间4程放大器,该放大器包括四组由凹面镜组成的空间滤波器,它们构成一个全像传递系统。由于有实焦点,放大器置于真空系统中。放大前,入射的高斯光束首先通过一个软边光阑和一个空间滤波器,被整形为高阶超高斯光束,然后在放大器中进行放大和像传递。钛宝石晶体置于各空间滤波器的像面和物面上。实验表明,该放大器结构稳定,可以保持良好的光束质量,远场焦斑达到近一倍衍射极限。     

A parametric imaging approach for the segmentation of ultrasound data

When an ultrasonic examination is performed, a segmentation tool would often be very useful, either for the detection of pathologies, the early diagnosis of cancer or the follow-up of the lesions. Such a tool must be both reliable and accurate. However, because of the relatively reduced quality of ultrasound images due to the speckled texture, the segmentation of ultrasound data is a difficult task. We have previously proposed to tackle the problem using a multiresolution Bayesian region-based algorithm. For computation time purposes, a multiresolution version has been implemented. In order to improve the quality of the segmentation, we propose to perform the segmentation not only from the envelope image but to combine more information about the properties of the tissues in the segmentation process. Several acoustical parameters have thus been computed, either directly from the images or from the radio-frequency (RF) signal.

In a previous study, two parametric images were involved in the segmentation process. The parameter represented the integrated backscatter (IBS) and the mean central frequency (MCF), which is a measurement related to the attenuation of ultrasound waves in the media. In this study, parameters representative of the scattering conditions in the tissue are evaluated in the multiparametric segmentation process. They are extracted from the K-distribution (,b) and the Nakagami distribution (m,Ω) and are related to the local density of scatterers (,m), the size of the scatterers (b) and the backscattering properties of the medium (Ω).

The acoustical features are calculated locally on a sliding window. This procedure allows to built parametric mapping representing the particular characteristics of the medium. To test the influence of the acoustical parameters in the segmentation process, a set of numerical phantoms has been computed using the Field software developed by J.A. Jensen. Each phantom consists in two regions with two different acoustical properties: the density of scatterers and the scattering amplitude. From both the simulated RF signals and envelope images, the parameters have been computed; their relevance to represent a particular characteristic of the medium is evaluated. The segmentation has been processed for each phantom. The ability of each parameter to improve the segmentation results is validated. A agar–gel phantom has also been created, in order to test the accuracy of the parameters in conditions closer to the in vivo ultrasound imaging. This phantom contains four inclusions with different concentrations of silica. A B&K ultrasound device provides the RF data. The quantification of the segmentation quality is based on the rate of correctly classified pixels and it has been computed for all the parameters either from the field images or the phantom images. The large improvement in the segmentation results obtained reveals that the multiparametric segmentation scheme proposed in this study can be a reliable tool for the processing of noisy ultrasound data.     

A novel hybrid color image encryption algorithm using two complex chaotic systems

Based on complex Chen and complex Lorenz systems, a novel color image encryption algorithm is proposed. The larger chaotic ranges and more complex behaviors of complex chaotic systems, which compared with real chaotic systems could additionally enhance the security and enlarge key space of color image encryption. The encryption algorithm is comprised of three step processes. In the permutation process, the pixels of plain image are scrambled via two-dimensional and one-dimensional permutation processes among RGB channels individually. In the diffusion process, the exclusive-or (XOR for short) operation is employed to conceal pixels information. Finally, the mixing RGB channels are used to achieve a multilevel encryption. The security analysis and experimental simulations demonstrate that the proposed algorithm is large enough to resist the brute-force attack and has excellent encryption performance.     

An image encryption scheme based on the MLNCML system using DNA sequences

We propose a new image scheme based on the spatiotemporal chaos of the Mixed Linear–Nonlinear Coupled Map Lattices (MLNCML). This spatiotemporal chaotic system has more cryptographic features in dynamics than the system of Coupled Map Lattices (CML). In the proposed scheme, we employ the strategy of DNA computing and one time pad encryption policy, which can enhance the sensitivity to the plaintext and resist differential attack, brute-force attack, statistical attack and plaintext attack. Simulation results and theoretical analysis indicate that the proposed scheme has superior high security.     

A novel method for detecting and counting overlapping tracks in SSNTD by image processing techniques

Overlapping object detection and counting is a challenge in image processing. A new method for detecting and counting overlapping circles is presented in this paper. This method is based on pattern recognition and feature extraction using “neighborhood values“ in an object image by implementation of image processing techniques. The junction points are detected by assignment of a value for each pixel in an image. As is shown, the neighborhood values for junction points are larger than the values for other points. This distinction of neighborhood values is the main feature which can be utilized to identify the junction points and to count the overlapping tracks. This method can be used for recognizing and counting charged particle tracks, blood cells and also cancer cells. The method is called “Track Counting based on Neighborhood Values” and is symbolized by “TCNV”.     

An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing

A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images.Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s).In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm.Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.     

Real-time automatic registration in optical surgical navigation

An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.     

A novel evaluation metric based on visual perception for moving target detection algorithm

Traditional performance evaluation index for moving target detection algorithm, whose each index’s emphasis is different when it is used to evaluate the performance of the moving target detection algorithm, is inconvenient for people to make an evaluation of the performance of algorithm comprehensively and objectively. Particularly, when the detection results of different algorithms have the same number of the foreground point and the background point, the algorithm’s each traditional index is the same, and we can’t use the traditional index to compare the performance of the moving target detection algorithms, which is the disadvantage of traditional evaluation index that takes pixel as a unit when calculating the index. To solve this problem, combining with the feature of human’s visual perception system, this paper presents a new evaluation index-Visual Fluctuation (VF) based on the principle of image block to evaluate the performance of moving target detection algorithm. Experiments showed that the new evaluation index based on the visual perception makes up for the deficiency of traditional one, and the calculation results are not only in accordance with visual perception of human, but also evaluate the performance of the moving target detection algorithm more objectively.     

稀疏测点条件下的结构法向速度重建

为了实现稀疏测点条件下结构表面法向速度的准确重建,利用声辐射模态包含结构表面几何形状信息的性质,以声辐射模态作为基函数提出了一种稀疏测点条件下的结构法向振速重建方法。首先对结构表面的声辐射模态进行计算,并建立结构表面法向振动速度与声辐射模态之间的关系;在此基础上,由实际布置情况形成测点位置处的振速与其声辐射模态值的关系,并通过最小二乘法求得展开系数;最后由展开系数重建出结构表面的全部法向振速。利用两端封闭的双层钢质圆柱壳体在消声水池中进行了试验验证,分别开启激振器和转子台进行激励,两个试验的结果均表明,当测点数目较少时,所测的结果不能准确地表示结构的实际振动情况,在波数域内就表现为与振动相关的波数成份的丢失;利用所提出的方法,可以较为准确地重建和恢复结构的表面法向速度及其波数成份,由此验证了所提出方法的有效性。