Application of wavelet denoising to improve compression efficiency while preserving integrity of digital micrographs

Modern microscopy methods require efficient image compression techniques owing to collection of up to thousands of images per experiment. Current irreversible techniques such as JPEG and JPEG2000 are not optimized to preserve the integrity of the scientific data as required by 21 CFR part 11. Therefore, to construct an irreversible, yet integrity-preserving compression mechanism, we establish a model of noise as a function of signal in our imaging system. The noise is then removed with a wavelet shrinkage algorithm whose parameters are adapted to local image structure. We ascertain the integrity of the denoised images by measuring changes in spatial and intensity distributions of registered light in the biological images and estimating changes of the effective microscope MTF. We demonstrate that the proposed denoising procedure leads to a decrease in image file size when a reversible JPEG2000 coding is used and provides better fidelity than irreversible JPEG and JPEG2000 at the same compression ratio. We also demonstrate that denoising reduces image artefacts when used as a pre-filtering step prior to irreversible image coding.     

基于MATLAB的JPEG量化算法研究

以DCT变换为核心的JPEG图像压缩关键技术在于量化表的选取,量化表的选取决定了图像信息的损失程度和恢复图像质量。由于MATLAB语言具备高效、可视化及推理能力强等特点,为教学研究带来了极大的方便性,但目前还没有一套完整的在MATLAB环境下实现JPEG压缩全部流程的系统程序。文中以模块化方式编写了一套基于MATLAB的J PEG图像压缩和恢复所必需的函数构成完整的系统,提出了一种简单易行的可控制压缩比算法。通过大量实验数据分析和比较了几种效果较好且易于实现的修正建议量化表的算法,并重点就如何结合人眼的视觉特性和根据图像自身的特点寻找较好的量化算法等问题作初步的探讨。     

Image signal-to-noise ratio and noise variance estimation using autoregressive model

During the last three decades, several techniques have been proposed for signal-to-noise ratio (SNR) and noise variance estimation in images, with different degrees of success. Recently, a novel technique based on the statistical autoregressive model (AR) was developed and proposed as a solution to SNR estimation in scanning electron microscope (SEM) image. In this paper, the efficiency of the developed technique with different imaging systems is proven and presented as an optimum solution to image noise variance and SNR estimation problems. Simulation results are carried out with images like Lena, remote sensing, and SEM. The two image parameters, SNR and noise variance, are estimated using different techniques and are compared with the AR-based estimator.     

视觉传感器网络协作块压缩感知图像传输方法

JPEG2000等现有图像压缩传输算法虽然压缩性能好,但复杂度高,资源受限的视觉传感器节点很难适应.基于压缩感知理论研究视觉传感器网络环境下的图像传输问题.首先设计主-从节点对模型,分块监测图像;然后研究协作块压缩感知理论,基于图像分块和节点协作来减少测量矩阵的维数,降低压缩感知的计算复杂度;对处在发送端的主从节点,提出尽力而为的压缩感知测量值传输方法,以最大化图像重构质量为目标,尽力传送最多的测量值;而在接收端,汇聚节点依据内积准则来判断重构质量,确定传输终止条件,避免多余的测量值传输,节省传输耗能.实验结果表明,和经典压缩感知相比较,4个视觉传感器节点协作的块压缩感知传输方法将测量矩阵维数减少了2个量级,且有更少的网络耗能.     

数字X光医学影像压缩技术

针对数字X光影像仪(CR)影像的特点以及CR成像与成像板(IP)密切关系,提出一种基于图像阈值分割,并对分割的不同区域采用不同压缩方法的数字X光(CR)医学影像压缩技术.首先分割出暗背景区,进行中值滤波,然后行程编码,再根据IP板与X光曝光剂量的关系,将感兴趣区12bit像素降低量化级别降低为9.3bit来降低数据量,并采用嵌入式小波变换方法实现近无损压缩.实验结果表明,在高压缩比条件下,感兴趣区仍具有较高的峰值信噪比(PSNR),保征了感兴趣区的图像质量,具有良好的视觉效果.     

无线传感网基于邻居簇的JPEG2000多节点协同实现

针对无线传感器网络能量、存储、处理能力严重受限的特点,基于"在网计算"的思想,结合JPEG2000算法流程以及无线传感器网络的网络拓扑结构特点,提出一种基于邻居簇的JPEG2000多节点协同图像压缩方法。首先,将相机节点采集的图像分片,并根据图像的梯度幅度值进行压缩比特率的自适应优化分配。然后,将压缩任务转移到多个邻居簇内,以最小化网络总能耗为目标,由多节点协作共同完成图像压缩和传输。仿真结果表明,该方法不仅使无线传感器网络中实现大尺寸图像的JPEG2000编码成为可能,且相机节点能耗仅为压缩图像后传输至基站方案的3.4%,极大地平衡了网络节点能耗,使网络生命周期提高了7倍以上。     

面向深空探测Bayer图像的高效编码

本文提出一种基于JPEG2000标准的Bayer图像高性能RBCR（Remove Bayer Component Relation, RBCR）压缩算法。在RBCR压缩算法中,根据Bayer图像相关性较高的特点,对Bayer图像进行颜色分量分离,得到处理单元子图,对各子图进行1*4整型离散余弦变换,降低Bayer图像中各颜色分量空间域内的相关性;对变换后的各分量DCT系数使用标准JPEG2000算法独立完成小波变换、Tier1编码、MQ编码和率失真斜率计算等,再基于率失真斜率联合截取方法完成各个分量的码流截取,即使用相同的率失真斜率门限值,按照率失真斜率值由高到低的顺序依次完成所有分量编码码块的码流截取,最后各个分量的截取结果再进行独立的码流组织输出。在RBCR算法中通过加入DCT变换降低Bayer图像相关性和对各个分量码流的率失真斜率联合截取,提高恢复图像质量且精确控制了码率。实验结果表明,RBCR算法与各个分量独立压缩方法相比,恢复图像质量得到提升,尤其在4倍的压缩倍数下效果最佳,峰值信噪比平均提高1.814dB,复杂峰值信噪比平均提高2.414dB,可以满足深空探测低复杂度和高质量图像的要求。     

无线传感器网络中基于邻居簇的JPEG2000多节点协同图像压缩方法

针对无线传感器网络能量、存储、处理能力严重受限的特点,一些学者提出了“在网计算”的思想,对实现无线传感器网络中大尺寸、高分辨率图像的压缩和传输极具指导意义。结合JPEG2000算法流程以及无线传感器网络的网络拓扑结构特点,本文提出一种基于邻居簇的JPEG2000多节点协同图像压缩方法,即将相机节点采集的图像分片,然后将压缩任务转移到多个邻居簇内,由多节点协作共同完成。仿真结果表明,该方法不仅使无线传感器网络中实现大尺寸图像的JPEG2000编码成为可能,且极大地缓解了相机节点的能耗压力,进而延长网络生命周期。     

An open‐source deconvolution software package for 3‐D quantitative fluorescence microscopy imaging

Deconvolution techniques have been widely used for restoring the 3‐D quantitative information of an unknown specimen observed using a wide‐field fluorescence microscope. Deconv , an open‐source deconvolution software package, was developed for 3‐D quantitative fluorescence microscopy imaging and was released under the GNU Public License. Deconv provides numerical routines for simulation of a 3‐D point spread function and deconvolution routines implemented three constrained iterative deconvolution algorithms: one based on a Poisson noise model and two others based on a Gaussian noise model. These algorithms are presented and evaluated using synthetic images and experimentally obtained microscope images, and the use of the library is explained. Deconv allows users to assess the utility of these deconvolution algorithms and to determine which are suited for a particular imaging application. The design of Deconv makes it easy for deconvolution capabilities to be incorporated into existing imaging applications.     

Confocal microscopic image sequence compression using vector quantization and three-dimensional pyramid

The three-dimensional (3-D) pyramid compressor project at the University of Glasgow has developed a compressor for images obtained from confocal laser scanning microscopy (CLSM) device. The proposed method using a combination of image pyramid coder and vector quantization techniques has good performance at compressing confocal volume image data. An experiment was conducted on several kinds of CLSM data using the presented compressor compared with other well-known volume data compressors, such as MPEG-1. The results showed that the 3-D pyramid compressor gave a higher subjective and objective image quality of reconstructed images at the same compression ratio, and presented more acceptable results when applying image processing filters on reconstructed images.     

数字图像压缩技术的研究及进展

数字图像压缩技术对于数字图像信息在网络上实现快速传输和实时处理具有重要的意义。本文介绍了当前几种最为重要的图像压缩算法：JPEG、JPEG2000、分形图像压缩和小波变换图像压缩，总结了它们的优缺点及发展前景。然后简介了任意形状可视对象编码算法的研究现状，并指出此算法是一种产生高压缩比的图像压缩算法。     

Constraint factor graph cut–based active contour method for automated cellular image segmentation in RNAi screening

Image‐based, high throughput genome‐wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time‐consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome‐wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale‐adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi‐channel image screening data.     

Evaluating image reconstruction methods in improving effective parameters on image quality in IRI-microPET

We intend to improve the image reconstruction for RMS contrast, spatial resolution and signal-to-noise (SNR) parameters for the animal positron emission tomograph IRI—microPET (IRI-Islamic Republic of Iran), designed and built at the Gamma scan laboratory of nuclear science and technology research institute. Acquired images quality from this system depends on different algorithms for image reconstruction in addition to its design and construction. In this paper, system features and tomography method are considered, firstly. Then, image reconstruction algorithms (MLEM, SART, and FBP) were performed on sinogarm. Acquired images quality from these reconstructed algorithms was compared with RMS contrast, spatial resolution and SNR characteristics. Also, reconstructed time and speed of process for three algorithms was considered. According to results, obtained RMS contrast, spatial resolution and signal to noise ratio (SNR) from reconstructed images with MLEM algorithm shows superiority of MLEM algorithm against the SART and FBP algorithms but its computation time is high. Thus, SART algorithm can be suitable replacement for MLEM algorithm.     

基于车载多媒体视音系统中图片压缩解压缩算法的研究

对计算机屏幕图像的压缩一般是直接用传统的RLE、LZW或JPEG算法.以上算法对微控制器系统中图片压缩难以达到压缩比和压缩速度的兼顾.本文提出了一种多级分块压缩算法.首先将屏幕图片分成复杂图形块和简单图形块.然后对简单块采用无失真压缩算法.对复杂块采用有失真压缩算法压缩.解压缩时对不同的块分别用逆压缩算法进行解压缩,最后再把各个块复合成一副图片.试验证明,本文所提出的压缩算法对车载多媒体视音系统中图片压缩比较大,压缩和解压缩速度快,性能优于传统的压缩算法.     

Background intensity correction for terabyte‐sized time‐lapse images

Several computational challenges associated with large‐scale background image correction of terabyte‐sized fluorescent images are discussed and analysed in this paper. Dark current, flat‐field and background correction models are applied over a mosaic of hundreds of spatially overlapping fields of view (FOVs) taken over the course of several days, during which the background diminishes as cell colonies grow. The motivation of our work comes from the need to quantify the dynamics of OCT‐4 gene expression via a fluorescent reporter in human stem cell colonies. Our approach to background correction is formulated as an optimization problem over two image partitioning schemes and four analytical correction models. The optimization objective function is evaluated in terms of (1) the minimum root mean square (RMS) error remaining after image correction, (2) the maximum signal‐to‐noise ratio (SNR) reached after downsampling and (3) the minimum execution time. Based on the analyses with measured dark current noise and flat‐field images, the most optimal GFP background correction is obtained by using a data partition based on forming a set of submosaic images with a polynomial surface background model. The resulting image after correction is characterized by an RMS of about 8, and an SNR value of a 4 × 4 downsampling above 5 by Rose criterion. The new technique generates an image with half RMS value and double SNR value when compared to an approach that assumes constant background throughout the mosaic. We show that the background noise in terabyte‐sized fluorescent image mosaics can be corrected computationally with the optimized triplet (data partition, model, SNR driven downsampling) such that the total RMS value from background noise does not exceed the magnitude of the measured dark current noise. In this case, the dark current noise serves as a benchmark for the lowest noise level that an imaging system can achieve. In comparison to previous work, the past fluorescent image background correction methods have been designed for single FOV and have not been applied to terabyte‐sized images with large mosaic FOVs, low SNR and diminishing access to background information over time as cell colonies span entirely multiple FOVs. The code is available as open‐source from the following link https://isg.nist.gov/ .     

Performance of signal‐to‐noise ratio estimation for scanning electron microscope using autocorrelation Levinson–Durbin recursion model

A new technique to quantify signal‐to‐noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson–Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first‐order linear interpolation and nearest neighbourhood combined with first‐order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation.     

Semi-automated imaging system to quantitate estrogen and progesterone receptor immunoreactivity in human breast cancer

A semi‐automated imaging system is described to quantitate estrogen and progesterone receptor immunoreactivity in human breast cancer. The system works for any conventional method of image acquisition using microscopic slides that have been processed for immunohistochemical analysis of the estrogen receptor and progesterone receptor. Estrogen receptor and progesterone receptor immunohistochemical staining produce colorimetric differences in nuclear staining that conventionally have been interpreted manually by pathologists and expressed as percentage of positive tumoral nuclei. The estrogen receptor and progesterone receptor status of human breast cancer represent important prognostic and predictive markers of human breast cancer that dictate therapeutic decisions but their subjective interpretation result in interobserver, intraobserver and fatigue variability. Subjective measurements are traditionally limited to a determination of percentage of tumoral nuclei that show positive immunoreactivity. To address these limitations, imaging algorithms utilizing both colorimetric (RGB) as well as intensity (gray scale) determinations were used to analyze pixels of the acquired image. Image acquisition utilized either scanner or microscope with attached digital or analogue camera capable of producing images with a resolution of 20 pixels /10 μ. Areas of each image were screened and the area of interest richest in tumour cells manually selected for image processing. Images were processed initially by JPG conversion of SVS scanned virtual slides or direct JPG photomicrograph capture. Following image acquisition, images were screened for quality, enhanced and processed. The algorithm‐based values for estrogen receptor and progesterone receptor percentage nuclear positivity both strongly correlated with the subjective measurements (intraclass correlation: 0.77; 95% confidence interval: 0.59, 0.95) yet exhibited no interobserver, intraobserver or fatigue variability. In addition the algorithms provided measurements of nuclear estrogen receptor and progesterone receptor staining intensity (mean, mode and median staining intensity of positive staining nuclei), parameters that subjective review could not assess. Other semi‐automated image analysis systems have been used to measure estrogen receptor and progesterone receptor immunoreactivity but these either have required proprietary hardware or have been based on luminosity differences alone. By contrast our algorithms were independent of proprietary hardware and were based on not just luminosity and colour but also many other imaging features including epithelial pattern recognition and nuclear morphology. These features provide a more accurate, versatile and robust imaging analysis platform that can be fully automated in the near future. Because of all these properties, our semi‐automated imaging system ‘adds value’ as a means of measuring these important nuclear biomarkers of human breast cancer.     

强反射表面缺陷图像预处理

为了校正强反射表面图像的亮度不均匀性,抑制局部强亮度现象,同时使其保留足够多的原始图像信息,研究了强反射表面缺陷图像的预处理方法。分析了强反射表面的光学特性,研究了偏微分方程理论及同态滤波算法,在此基础上提出了一种基于偏微分方程的同态滤波算法,即采用热传导方程滤波算子对同态分解的图像进行处理。引入信息熵对不同尺度参数下的处理效果进行评价,从而确定热传导同态滤波的尺度参数,并将该算法与背景去除法、基于小波的同态滤波算法进行了比较。对比实验表明,采用提出的算法处理后得到的图像其灰度级均匀分布在1～6之间,信息熵值均在91%以上。该方法校正了图像亮度的不均匀性,提高了图像质量,同时能保留足够多的原始图像信息。     

三维显微图像带约束的迭代解卷积复原算法

阐述显微镜透明厚样本成像原理和三维显微图像带约束的迭代解卷积复原算法。根据显微镜透明厚样本成像原理,对已知三维清晰图像进行退化处理,并且使用带约束的迭代解卷积算法去除退化图像中的散焦信息。试验结果表明,图像散焦信息的干扰得到有效的去除,清晰度和信噪比得到明显的改善,并且该算法可以恢复成像过程中丢失的部分频率成分,实现超分辨率复原。当迭代次数较大时,复原效果优于邻域法和线性方法。     

基于小波与双三次插值的高温锻件红外图像增强方法

提出了一种基于小波变换和双三次插值的图像处理方法,该方法利用离散小波将原图像分解成低频子带与高频子带,然后对原图像及这些子带分别进行双三次插值,同时提出增加一个中间步骤来估算高频子带,即扣除原低分辨率图像和插值后的LL子带中的相同成分而得到不同成分的图像（高频成分）,然后利用不同成分的图像对高频子带LH、HL及HH分别进行校正融合,并通过小波逆变换对这些图像进行融合重构。该方法不仅能够有效地消除图像噪声,改善细节信息,而且能够最大限度地保留边缘信息和增加图像的清晰度。最后运用该方法和传统算法分别对核电主管道高温锻件和小型高温锻件的红外图像进行增强处理,实验结果验证了该方法的可行性及优越性。