Superiority of 3D wavelet-packet denoising in MR microscopy

Three dimensional Magnetic Resonance Imaging (MRI) datasets are becoming increasingly important in clinical and research applications because of their inherent signal to noise (SNR) advantages, high resolution and isotropic voxels. Despite SNR advantages, some 3D acquisitions may be SNR-limited, particularly in MR microscopy. Historically, both classic filtering and wavelet-based denoising techniques have been performed on a slice-by-slice basis. In principle, adaptive techniques such as best- basis wavelet-packet denoising might offer inherent advantages when performed in 3D, instead of 2D, by tracking through plane "structure" and suppressing noise "pseudostructure." This hypothesis was tested in 10 volumetric MR microscopy datasets from several different MR microscopy atlas projects. 3D wavelet-packet denoised images consistently yielded lower minimum mean-square error and subjectively perceived noise power than corresponding 2D denoised images using otherwise identical algorithms and parameters. MR microscopy researchers preferred the denoised images to the unprocessed images for their atlas projects.     

A novel image fusion method using WBCT and PCA

A novel image fusion algorithm based on wavelet-based contourlet transform （WBCT） and principal component analysis （PCA） is proposed. The PCA method is adopted for the low-frequency components. Using the proposed algorithm to choose the greater of the active measures, the region consistency test is performed for the high-frequency components. Experiments show that the proposed method works better in preserving the edge and texture information than wavelet transform method and Laplacian pyramid （LP） method do in image fusion. Four indicators for the fusion image are given to compare the proposed method with other methods.     

Color image edge detection method using VTV denoising and color difference

The traditional Canny edge uses Gaussian filter to suppress the noise, it also smoothes out the image edges. An improved Canny edge detection method for color image is proposed in this paper, the improved method uses fast vectorial total variation (VTV) minimization model to remove noise in color image, and then calculates the color difference and direction in CIELAB color space, which is used for non-maximal suppression. Finally, the improved method extracts the edges by the double-threshold method. The experimental results show that the proposed method achieves better performance than the traditional Canny edge detector. It can remove noise while preserving the image edges, and effectively detect the image edges.     

An MRI denoising method using image data redundancy and local SNR estimation

This paper presents an LMMSE-based method for the three-dimensional (3D) denoising of MR images assuming a Rician noise model. Conventionally, the LMMSE method estimates the noise-less signal values using the observed MR data samples within local neighborhoods. This is not an efficient procedure to deal with this issue while the 3D MR data intrinsically includes many similar samples that can be used to improve the estimation results. To overcome this problem, we model MR data as random fields and establish a principled way which is capable of choosing the samples not only from a local neighborhood but also from a large portion of the given data. To follow the similar samples within the MR data, an effective similarity measure based on the local statistical moments of images is presented. The parameters of the proposed filter are automatically chosen from the estimated local signal-to-noise ratio. To further enhance the denoising performance, a recursive version of the introduced approach is also addressed. The proposed filter is compared with related state-of-the-art filters using both synthetic and real MR datasets. The experimental results demonstrate the superior performance of our proposal in removing the noise and preserving the anatomical structures of MR images.     

A fast image encryption method by using chaotic 3D cat maps

An image encryption method using a chaotic 3D cat map is presented in this paper. The process of the proposed algorithm contains the simultaneous operations of pixels’ locations permutation and pixels’ values substitution at every iterative step of the chaotic map, which making the forward and reverse encryption needs only one traverse of the image pixels. Moreover, a perturbation is introduced to eliminate the undesirable finite precision effect of computer in realization. The main advantages of such a secure method are the simplicity and efficiency. Both simulations and analysis show the proposed algorithm can produce a large key space and resist the common existing cipher attacks. These good cryptographic properties make it suitable for image applications.     

Wavelet-based image denoising using variance field diffusion

Wavelet shrinkage is an image restoration technique based on the concept of thresholding the wavelet coefficients. The key challenge of wavelet shrinkage is to find an appropriate threshold value, which is typically controlled by the signal variance. To tackle this challenge, a new image restoration approach is proposed in this paper by using a variance field diffusion, which can provide more accurate variance estimation. Experimental results are provided to demonstrate the superior performance of the proposed approach.     

Content-based adaptive image denoising using spatial information

The maximum a posteriori (MAP) model is widely used in image processing fields, such as denoising, deblurring, segmentation, reconstruction, and others. However, the existing methods usually employ a fixed prior item and regularization parameter for the whole image and ignore the local spatial adaptive properties. Though the non-local total variation model has shown great promise because of exploiting the correlation in the image, the computation cost and memory load are the issues. In this paper, a content-based local spatial adaptive denoising algorithm is proposed. To realize the local spatial adaptive process of the prior model and regularization parameter, first the degraded image is divided into several same-sized blocks and the Tchebichef moment is used to analyze the local spatial properties of each block. Different property prior items and regularization parameters are then applied adaptively to different properties’ blocks. To reduce the computational load in denoising process, the split Bregman iteration algorithm is employed to optimize the non-local total variation model and accelerate the speed of the image denoising. Finally, a set of experiments and performance evaluation using recent image quality assessment index are provided to assess the effectiveness of the proposed method.     

A general strategy for anisotropic diffusion in MR image denoising and enhancement

Anisotropic diffusion (AD) has proven to be very effective in the denoising of magnetic resonance (MR) images. The result of AD filtering is highly dependent on several parameters, especially the conductance parameter. However, there is no automatic method to select the optimal parameter values. This paper presents a general strategy for AD filtering of MR images using an automatic parameter selection method. The basic idea is to estimate the parameters through an optimization step on a synthetic image model, which is different from traditional analytical methods. This approach can be easily applied to more sophisticated diffusion models for better denoising results. We conducted a systematic study of parameter selection for the AD filter, including the dynamic parameter decreasing rate, the parameter selection range for different noise levels and the influence of the image contrast on parameter selection. The proposed approach was validated using both simulated and real MR images. The model image generated using our approach was shown to be highly suitable for the purpose of parameter optimization. The results confirm that our method outperforms most state-of-the-art methods in both quantitative measurement and visual evaluation. By testing on real images with different noise levels, we demonstrated that our method is sufficiently general to be applied to a variety of MR images.     

一种改进的小波阈值去噪方法

提出了一种新的阈值函数,它高阶可导,便于各种数学运算。与硬阈值函数相比,它在阈值点处是连续的;相比于软阈值函数,减小了估计小波系数存在时的恒定偏差,更好地保留了图像边缘等局部特征。选用平移不变小波变换,结合新提出的阈值函数,给出了一种新的去噪方法。仿真实验结果证明了此方法的优越性。     

小波阈值去噪的一种改进方法

白噪声的方差和幅值随着小波变换尺度的增加会逐渐减小,而信号的方差和幅值与小波变换的尺度变化无关。本文在Donoho的软、硬阈值去噪方法基础上,提出了一种新的阈值函数,并把它们应用在图像的去噪上。该阈值函数具有物理意义清晰、表达式简单、计算方便等优点。实际噪声图像测试结果表明,这种经改进的方法可以有效地去除白噪声干扰,无论是在视觉效果上还是在信噪比和均方误差定量指标上均明显优于常用的软、硬阈值去噪算法以及改进的软硬阈值折中算法。     

Computational integral imaging reconstruction method of 3D images using pixel-to-pixel mapping and image interpolation

In this paper, we propose a novel computational integral imaging reconstruction (CIIR) method to improve the visual quality of the reconstructed images using a pixel-to-pixel mapping and an interpolation technique. Since an elemental image is magnified inversely through the corresponding pinhole and mapped on the reconstruction output plane based on pinhole-array model in the conventional CIIR method, the visual quality of reconstructed output image (ROI) degrades due to the interference problem between adjacent pixels during the superposition of the magnified elemental images. To avoid this problem, the proposed CIIR method generates dot-pattern ROIs using a pixel-to-pixel mapping and substitutes interpolated values for the empty pixels within the dot-pattern ROIs using an interpolation technique. The interpolated ROIs provides a much improved visual quality compared with the conventional method because of the exact regeneration of pixel positions sampled in the pickup process without interference between pixels. Moreover, it can enable us to reduce a computational cost by eliminating the magnification process used in the conventional CIIR. To confirm the feasibility of the proposed system, some experiments are carried out and the results are presented.     

基于小波变换的共焦拉曼图像去噪方法

针对现有共焦拉曼光谱显微图像噪声去除不足的问题,提出一种基于小波变换的拉曼图像全面去噪方法。首先对拉曼光谱图像进行小波变换处理,然后再对拉曼图像进行双边滤波处理,进而实现拉曼光谱显微图像中噪声干扰的抑制。理论分析和实验表明:与现有拉曼光谱显微图像去噪方法相比,所提方法使拉曼光谱显微图像的峰值信噪比和结构相似度分别改善了92.99%和197%,其有效提高了拉曼光谱图像的信噪比和物质成分分析的准确率。     

3D image registration using a fast noniterative algorithm

This note describes the implementation of a three-dimensional (3D) registration algorithm, generalizing a previous 2D version [Alexander, Int J Imaging Systems and Technology 1999;10:242-57]. The algorithm solves an integrated form of linearized image matching equation over a set of 3D rectangular sub-volumes ('patches') in the image domain. This integrated form avoids numerical instabilities due to differentiation of a noisy image over a lattice, and in addition renders the algorithm robustness to noise. Registration is implemented by first convolving the unregistered images with a set of computationally fast [O(N)] filters, providing four bandpass images for each input image, and integrating the image matching equation over the given patch. Each filter and each patch together provide an independent set of constraints on the displacement field derived by solving a set of linear regression equations. Furthermore, the filters are implemented at a variety of spatial scales, enabling registration parameters at one scale to be used as an input approximation for deriving refined values of those parameters at a finer scale of resolution. This hierarchical procedure is necessary to avoid false matches occurring. Both downsampled and oversampled (undecimating) filtering is implemented. Although the former is computationally fast, it lacks the translation invariance of the latter. Oversampling is required for accurate interpolation that is used in intermediate stages of the algorithm to reconstruct the partially registered from the unregistered image. However, downsampling is useful, and computationally efficient, for preliminary stages of registration when large mismatches are present. The 3D registration algorithm was implemented using a 12-parameter affine model for the displacement: u(x) = Ax + b. Linear interpolation was used throughout. Accuracy and timing results for registering various multislice images, obtained by scanning a melon and human volunteers in various stationary positions, is described. The algorithm may be generalized to more general models of the displacement field, and is also well suited to parallel processing.     

一种新的基于小波变换的图像去噪方法

根据图像小波分解的特点和小波分解后高频小波系数的统计特性,构造了一种新阈值函数的去噪算法。与传统的硬阈值、软阈值函数相比,新阈值函数考虑了图像能量分布的特点,对于每一小波系数乘以一个与自身大小相关的降噪因子,并且新阈值函数简单易于计算,具有较强的自适应性。实验结果表明,采用新阈值函数的去噪结果能够有效地抑制图像的马赛克效应,无论在视觉效果上,还是在信噪比增益上均优于传统的软、硬阈值方法。     

基于粗糙集核优化的支持向量机在多组分污染气体定量分析中的研究与应用

研究基于粗糙集核优化的支持向量机(RS-SVM)在红外光谱定量中的应用。通过粗糙集分类的方法对多组分污染气体红外光谱对应的特征波长段进行核函数初始数据的优化,再将优化后的核函数带入支持向量机,从而将二维混合光谱信息投影到高维空间,再进行单种气体浓度的反演运算。通过采用LS-SVM和PCA-SVM两种典型的光谱数据处理算法作对比,对五种混合气体各组分定量分析进行比较。当光谱可分度高时,三种方法的预测值都接近标准值,平均误差接近于0.13;而当光谱可分度低时,RS-SVM的预测值比前两种更精确,且当待测种类越多时,该方法精度和运算时间的优势越显著。     

MR image reconstruction of sparsely sampled 3D k-space data by projection-onto-convex sets

In many rapid three-dimensional (3D) magnetic resonance (MR) imaging applications, such as when following a contrast bolus in the vasculature using a moving table technique, the desired k-space data cannot be fully acquired due to scan time limitations. One solution to this problem is to sparsely sample the data space. Typically, the central zone of k-space is fully sampled, but the peripheral zone is partially sampled. We have experimentally evaluated the application of the projection-onto-convex sets (POCS) and zero-filling (ZF) algorithms for the reconstruction of sparsely sampled 3D k-space data. Both a subjective assessment (by direct image visualization) and an objective analysis [using standard image quality parameters such as global and local performance error and signal-to-noise ratio (SNR)] were employed. Compared to ZF, the POCS algorithm was found to be a powerful and robust method for reconstructing images from sparsely sampled 3D k-space data, a practical strategy for greatly reducing scan time. The POCS algorithm reconstructed a faithful representation of the true image and improved image quality with regard to global and local performance error, with respect to the ZF images. SNR, however, was superior to ZF only when more than 20% of the data were sparsely sampled. POCS-based methods show potential for reconstructing fast 3D MR images obtained by sparse sampling.     

基于非分样ridgelet 标架的图像滤噪恢复新算法的研究

提出了一种基于非分样ridgelet标架的图像噪声滤除(UDRIFDA)的新算法。ridgelet标架的特点是:基函数不可分离变量且具有很强的方向性,能够实现对沿直线奇性的有效描述。离散非分样ridgelet标架是通过离散Radon变换切片上的一维非分样小波变换标架来实现的。由于非分样小波变换具有位移不变性,能够很好地刻画多尺度下一维信号的局部特征,基于一维非分样小波变换的软阈值去噪算法能够有效地降低一维信号急剧变化处所产生的震荡现象,故基于非分样ridgelet标架的图像滤噪算法能够大大降低恢复图像上的伪影,有效的克服了文献[1]中分样ridgelet标架滤噪算法(DRITDA)的缺陷。数值实验表明新算法较DRITDA和2D-DWT算法更能提高恢复图像的信噪比和视觉质量。     

Image-guided regularization level set evolution for MR image segmentation and bias field correction

Magnetic resonance (MR) image segmentation is a crucial step in surgical and treatment planning. In this paper, we propose a level-set-based segmentation method for MR images with intensity inhomogeneous problem. To tackle the initialization sensitivity problem, we propose a new image-guided regularization to restrict the level set function. The maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework. Under this framework, both the contour prior and the bias field prior are fully used. As a result, the image intensity inhomogeneity can be well solved. Extensive experiments are provided to evaluate the proposed method, showing significant improvements in both segmentation and bias field correction accuracies as compared with other state-of-the-art approaches.