Medical image fusion based on nuclear norm minimization

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image‐guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most approaches have not touched the low rank nature of matrix formed by medical image, which usually lead to fusion image distortion and image information loss. These methods also often lack universality when dealing with different kinds of medical images. In this article, we propose a novel medical image fusion to overcome aforementioned issues on existing methods with the aid of low rank matrix approximation with nuclear norm minimization (NNM) constraint. The workflow of our method is described as: firstly, nonlocal similar patches across the medical image are searched by block matching for local patch in source images. Second, a fused matrix is stacking by shared nonlocal similarity patches, then the low rank matrix approximation methods under nuclear norm minimization can be used to recover low rank feature of fused matrix. Finally, fused image can be gotten by aggregating all the fused patches. Experimental results show that the proposed method is superior to other methods in both subjectively visual performance and objective criteria. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 310–316, 2015     

Robust brain MRI denoising and segmentation using enhanced non‐local means algorithm

Image denoising is an integral component of many practical medical systems. Non‐local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non‐local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have proposed an enhanced non‐local means (ENLM) algorithm, for application to brain MRI, by introducing several extensions to the IANLM algorithm. First, a Rician bias correction method is applied for adapting the IANLM algorithm to Rician noise in MR images. Second, a selective median filtering procedure based on fuzzy c‐means algorithm is proposed as a postprocessing step, in order to further improve the quality of IANLM‐filtered image. Third, different parameters of the proposed ENLM algorithm are optimized for application to brain MR images. Different variants of the proposed algorithm have been presented in order to investigate the influence of the proposed modifications. The proposed variants have been validated on both T1‐weighted (T1‐w) and T2‐weighted (T2‐w) simulated and real brain MRI. Compared with other denoising methods, superior quantitative and qualitative denoising results have been obtained for the proposed algorithm. Additionally, the proposed algorithm has been applied to T2‐weighted brain MRI with multiple sclerosis lesion to show its superior capability of preserving pathologically significant information. Finally, impact of the proposed algorithm has been tested on segmentation of brain MRI. Quantitative and qualitative segmentation results verify that the proposed algorithm based segmentation is better compared with segmentation produced by other contemporary techniques.     

Guided Intra-Patch Smoothing Graph Filtering for Single-Image Denoising

Graph filtering is an important part of graph signal processing and a useful tool for image denoising. Existing graph filtering methods, such as adaptive weighted graph filtering (AWGF), focus on coefficient shrinkage strategies in a graph-frequency domain. However, they seldom consider the image attributes in their graph-filtering procedure. Consequently, the denoising performance of graph filtering is barely comparable with that of other state-of-the-art denoising methods. To fully exploit the image attributes, we propose a guided intra-patch smoothing AWGF (AWGF-GPS) method for single-image denoising. Unlike AWGF, which employs graph topology on patches, AWGF-GPS learns the topology of superpixels by introducing the pixel smoothing attribute of a patch. This operation forces the restored pixels to smoothly evolve in local areas, where both intra- and inter-patch relationships of the image are utilized during patch restoration. Meanwhile, a guided-patch regularizer is incorporated into AWGF-GPS. The guided patch is obtained in advance using a maximum-a-posteriori probability estimator. Because the guided patch is considered as a sketch of a denoised patch, AWGF-GPS can effectively supervise patch restoration during graph filtering to increase the reliability of the denoised patch. Experiments demonstrate that the AWGF-GPS method suitably rebuilds denoising images. It outperforms most state-of-the-art single-image denoising methods and is competitive with certain deep-learning methods. In particular, it has the advantage of managing images with significant noise.     

Investigation of multi-frame image super-resolution based on adaptive self-learning methods

The goal of this paper is to introduce and demonstrate a new high-performance super-resolution (SR) method for multi-frame images. By combining learning-based and reconstruction-based SR methods, this paper proposes a multi-frame image super-resolution method based on adaptive self-learning. Using the adaptive self-learning method and recovery of high-frequency edge information, an initial high-resolution (HR) image containing effective texture information is obtained. The edge smoothness prior is then used to satisfy the global reconstruction constraint and enhance the quality of the HR image. Our results indicate that this method achieves better performance than several other methods for both simulated data and real-scene images.     

Adaptive clutter filtering based on sparse component analysis in ultrasound color flow imaging

An adaptive method based on the sparse component analysis is proposed for stronger clutter filtering in ultrasound color flow imaging (CFI). In the present method, the focal underdetermined system solver (FOCUSS) algorithm is employed, and the iteration of the algorithm is based on weighted norm minimization of the dependent variable with the weights being a function of the preceding iterative solutions. By finding the localized energy solution vector representing strong clutter components, the FOCUSS algorithm first extracts the clutter from the original signal. However, the different initialization of the basis function matrix has an impact on the filtering performance of FOCUSS algorithms. Thus, 2 FOCUSS clutter- filtering methods, the original and the modified, are obtained by initializing the basis function matrix using a predetermined set of monotone sinusoids and using the discrete Karhunen-Loeve transform (DKLT) and spatial averaging, respectively. Validation of 2 FOCUSS filtering methods has been performed through experimental tests, in which they were compared with several conventional clutter filters using simplistic simulated and gathered clinical data. The results demonstrate that 2 FOCUSS filtering methods can follow signal varying adaptively and perform clutter filtering effectively. Moreover, the modified method may obtain the further improved filtering performance and retain more blood flow information in regions close to vessel walls.     

图像对准系统中提高精度的软件补偿技术

本文根据分层制造对准系统的精度要求,研究了改善精度的软件补偿措施:设计了SUSAN和多帧平均降噪结合的无损滤波算法以减小滤波过程对标记图像产生的畸变;以频域能量谱函数作为系统的聚焦测度函数实现自动聚焦,消减了由离焦引入的对准误差;以校正残差均方差的大小和稳定性为依据确定了三次多项式畸变模型,校正后畸变引入标记定位误差小...     

Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of traditional ideal lowpass filter. In detail, we reassess the existing low-rank denoising method with adaptive regularizer learning (ARLLR) from the view of graph filtering. A shrinkage approach subsequently is presented on the graph frequency domain, where the components of noisy image are adaptively decreased in each band by calculating their component significances. As a result, it makes the proposed graph filtering more explainable and suitable for denoising. Meanwhile, we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method. Therefore, ARLLR can be treated as a special form of graph filtering. It not only enriches the theory of graph filtering, but also builds a bridge from the low-rank methods to the graph filtering methods. In the experiments, we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix. The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.     

基于线性混合小波基的图像去噪

单小波基由于时频特性难以与复杂的图像特征相匹配,限制了小波闽值算法在图像去噪效果上的进一步提高.提出了一种基于线性混合小波基的图像去噪方法,将多个不同特性的正交小波基进行线性混合构成一个新的小波基,用该混合小波基对图像进行分解后再通过阈值处理实现去噪.调节混合系数,可使混合小波基的时频特性与图像特征相匹配,从而提高小波阈值去噪效果.实验结果表明,该方法去噪效果优于参与混合的各单小波基去噪效果,其峰值信噪比(PSNR)最大可提高3.5 dB.     

An extended non‐local means algorithm: Application to brain MRI

Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient.     

散斑干涉条纹图的总变分去噪方法

去除散斑条纹图中的噪声是电子散斑干涉测量技术的关键问题.提出将总变分图像去噪方法应用于电子散斑干涉条纹图滤波过程中,并对保真系数进行了改进.用总变分模型定义图像的能量函数,利用变分法求得满足能量函数的最优解,将图像去噪过程转化为求解偏微分方程的过程.分别对计算机模拟的条纹图和实验获得的条纹图进行了测试,定性和定量分析的结果表明该技术能够在显著滤波的同时保持条纹的对比度.     

Fast Algorithms for the Anisotropic LLT Model in Image Denoising

In this paper, we propose a new projection method for solving a general minimization problems with two $L^1$-regularization terms for image denoising. It is related to the split Bregman method, but it avoids solving PDEs in the iteration. We employ the fast iterative shrinkage-thresholding algorithm (FISTA) to speed up the proposed method to a convergence rate $O$($k^-$$^2$). We also show the convergence of the algorithms. Finally, we apply the methods to the anisotropic Lysaker, Lundervold and Tai (LLT) model and demonstrate their efficiency.     

Image speckle noise denoising by a multi-layer fusion enhancement method based on block matching and 3D filtering

In order to improve speckle noise denoising of block matching and 3D filtering (BM3D) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3D) based on nonsubsampled contourlet transform (NSCT) has been proposed. The method designs an NSCT hard threshold denoising enhancement to preprocess the image, then uses fusion enhancement in NSCT domain to fuse the preliminary estimation results of images before and after the NSCT hard threshold denoising, finally, BM3D denoising is carried out with the fused image to obtain the final denoising result. Experiments on natural images and medical ultrasound images show that MLFE-BM3D method can achieve better visual effects than BM3D method, the peak signal to noise ratio (PSNR) of the denoised image is increased by 0.5?dB. The MLFE-BM3D method can improve the denoising effect of speckle noise in the texture region, and still maintain a good denoising effect in the smooth region of the image.     

基于多级LatLRR和HSI空间的彩色图像融合算法

目的 为提高红外与彩色可见光融合图像的可视性,更好地再现图像的对比度和色彩效果,提出一种基于多级低秩表示和HSI颜色空间的彩色图像融合算法.方法 首先利用RGB到HSI颜色空间转换,把彩色可见光RGB图像转化到HSI颜色空间,并分离H,S,I三通道.然后利用LatLRR对彩色可见光图像的I通道图像和红外图像进行二级分解,可得到显著的细节部分和基础部分,并将彩色可见光图像I通道和红外图像的细节部分采用核范数自适应加权融合策略进行融合,基础部分采用高斯模糊逻辑值自适应加权进行融合.最后把融合后的细节部分和基础部分相加产生新的I通道图像,结合H,S通道再转到RGB空间,得到融合图.结果 实验结果表明,文中算法得到的融合图主观上彩色失真度最小、场景细节最清晰、红外目标更突出,同时客观评价指标值上升约1％～24％.结论 文中算法是一种有效的算法,对彩色图像融合结果有较好的改善作用.     

Image SR-based NLM and DCNN improved IBP with cubic B-spline

ABSTRACT

Image super-resolution (SR) techniques aim to estimate high-resolution (HR) image from low-resolution (LR) image. Existing SR method has slow convergence and recovery of high-frequency details are inaccurate. To overcome these issues, two algorithms have been proposed for image SR based on non-local means improved iterative back projection (NLM-IIBP), deep convolutional neural network improved iterative back projection (DCNN-IIBP) to produce high-resolution images with low noise, minimal blur by restoring high-frequency details. In NLM-IIBP denoised images have been interpolated using cubic B-spline interpolation and processed using IIBP based on guided bilateral method. NLM preserves the edges effectively, but does not consider high dimensional information and over smoothing during noise minimization. To further improve the resolution, NLM is replaced by DCNN. DCNN denoising method suppresses different noises at different noise levels. The proposed algorithms have been analysed and compared with existing approaches using various parameters to prove the effectiveness.     

Non-local total variation regularization approach for image restoration under a Poisson degradation

Poisson noise (also known as shot or photon noise) arises due to the lack of information during the image acquisition phase, it is quite common in the field of microscopic or astronomical imaging applications. In this paper, we propose a non-local total variation regularization framework with a p-norm driven data-fidelity for denoising the Poissonian images. In precise, the energy functional is derived using a Maximum A Posteriori estimator of the Poisson probability density function. The diffusion amounts to a non-local total variation minimization process, which eventually preserves fine structures while denoising the data. The numerical solution is sought under a fast converging split-Bregman iterative scheme. The proposed model is compared visually and statistically with the state-of-the-art Poisson denoising models.     

Performance evaluation of partial differential equation models in electronic speckle pattern interferometry and the delta-mollification phase map method

The ordinary differential equation (ODE) and partial differential equation (PDE) image- processing methods have been applied to reduce noise and enhance the contrast of electronic speckle pattern interferometry fringe patterns. We evaluate the performance of a few representative PDE denoising models quantitatively with two parameters called image fidelity and speckle index, and then we choose a good denoising model. Combining this denoising model with the ODE enhancement method, we make it possible to perform contrast enhancement and denoising simultaneously. Second, we introduce the delta-mollification method to smooth the unwrapped phase map. Finally, based on PDE image processing, delta mollification and some traditional techniques, an approach of phase extraction from a single fringe pattern is tested for computer-simulated and experimentally obtained fringe patterns. The method works well under a high noise level and limited visibility and can extract accurate phase values.     

Generating B-Scans of the Environment With a Conventional Sonar

This paper examines the task of displaying more information about the environment using a conventional ranging sonar than is available in standard time-of-flight (TOF) maps. A conventional ranging sonar forms an environmental image that displays the information in the echo envelope, similar to a medical ultrasound image. The sonar performs rotational sector scans of simple objects and two complex environments containing various reflecting structures. In acquiring sonar data, we repeatedly reset the conventional sonar to generate a point process whose density relates to the echo amplitude. This point process is displayed as a grayscale image, called a brightness scan (B-scan), analogous to B-scans in medical ultrasound. We compare the information content of sonar B-scans to TOF maps for object classification and show B-scans to be richer. B-scan textures produced by rough surfaces and volumes containing random scatterers exhibit statistical invariance, similar to some organs within the body, suggesting the feasibility of automated classification. Image artifacts and means for their identification are discussed. The qualitative information present in sonar B-scans should lead to improved quantitative techniques for classifying objects.     

提高分辨率的带宽外推SAR成像算法

分析了合成孔径雷达(SAR)的图像信号模型,阐述了应用数据外推方法提高分辨率的可行性.提出一种最小方差谱估计和最小加权范数约束结合的非参数类数据外推方法.该方法外推SAR相位历史域信号有效带宽可得到较好的成像效果.仿真和实测数据处理证明了此方法的有效性,并给出了定量比较与分析.     

基于局部坐标二次微分的自适应全变分去噪复原

针对传统TV去噪复原算法以梯度模值作为图像的边缘检测算子,无法清晰地识别边缘和灰度渐变区及去除平坦区内的孤立噪声的问题,提出了一种基于局部坐标二次微分的边缘检测算子对传统模型进行改进。改进后的模型能根据各像素点的新检测算子信息,自适应选取复原模型中决定扩散强弱的参数,并且利用图像局部信息对正则化项和保真项进行加权。同时在数值实现上,采用一种基于梯度矢量的方向变化的方法来实现散度离散化,以更加有效地保留图像的局部细节信息。数值试验表明,该算法在克服灰度渐变区内的阶梯效应和保留图像的细节边缘方面明显优于传统算法。     

Wavelet-based partial discharge image denoising

An application of wavelet-based denoising to phase-resolved partial discharge images is presented. The basic principles of wavelet denoising analysis, with a special focus on image decomposition, as well as examples of hard- and soft denoising thresholding are reported. For the purposes of decomposition, the Deabuchies wavelet and wavelet packets at different levels were applied. Simulations are discussed and the results obtained during online measurements on a 6 kV/200 kW motor are presented. The method described is especially suited to cases in which an external additive noise uncorrelated with a partial discharge (PD) signal is present during acquisition, for example, in cables, transformers, rotating machines and gas-insulated switchgears. The fundamental issue in image recovery using wavelet denoising seems to be the choice of the threshold value and the type of the wavelet. Proper preprocessing is crucial prior to pattern recognition on the basis of a correlation with predefined PD forms. In addition, wavelet decomposition could be treated as lossy image compression in applications such as image internet transfer to/from external databases, in which only wavelet coefficients could be sent discarding the ones below a certain threshold level. The method presented can be applied during PD acquisition, for example, in high voltage cables, transformers, rotating machines and gas-insulated switchgears. The wavelet denoising processing will definitely find future applications in PD analysers, besides the boxcar accumulation method and spatial or FFT-based digital filtering