An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images

Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio‐marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre‐processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two‐step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images. Microsc. Res. Tech. 77:547–559, 2014. © 2014 Wiley Periodicals, Inc.     

Segmentation of touching cell nuclei using gradient flow tracking

Reliable cell nuclei segmentation is an important yet unresolved problem in biological imaging studies. This paper presents a novel computerized method for robust cell nuclei segmentation based on gradient flow tracking. This method is composed of three key steps: (1) generate a diffused gradient vector flow field; (2) perform a gradient flow tracking procedure to attract points to the basin of a sink; and (3) separate the image into small regions, each containing one nucleus and nearby peripheral background, and perform local adaptive thresholding in each small region to extract the cell nucleus from the background. To show the generality of the proposed method, we report the validation and experimental results using microscopic image data sets from three research labs, with both over-segmentation and under-segmentation rates below 3%. In particular, this method is able to segment closely juxtaposed or clustered cell nuclei, with high sensitivity and specificity in different situations.     

Clustered nuclei splitting via curvature information and gray‐scale distance transform

Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images; therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line‐based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an individual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker‐controlled watershed method and ellipse fitting method.     

面向挤出中聚合物形态的层析图像处理方法的研究

依据光学信息论中的多维随机变量的联合熵、条件熵、平均互信息之间关系和互信息的链式法则,提出了面向挤出中聚合物形态的层析图像处理方法.它包括层析图像中的背景与分离、目标标度变换、亚像素插补的图像预处理和多模式目标特征图像的提取和测量.实验表明,通过该方法不仅能将目标形态从目标与背景之比为1∶9的层析图像中分辨出来,而且能提高目标图像的空间分辨率30%,解决了由于目标图像信息量小、对比度差而使得层析图像测量方法难以在工程中应用的问题.     

Segmentation of confocal microscope images of cell nuclei in thick tissue sections

Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525), a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.     

磨粒图象背景光照不均匀分布图象预处理

针对磨粒图片背景亮度呈现分布不均的问题，采用两种计算机图象处理方式进行了预处理，得到比较完整的二值图。第一种方式是：在没有铁磨粒情况下采集背景亮度变化，通过数字处理得到背景亮度变化的差值，然后将亮度补偿到铁谱图上。试验结果表明，该方法在边缘信息损失很小的情况下，得到磨粒二值图。第二种是：在不能得到背景光的条件下，通过一阶和二阶差分处理及其变形方法，来消除背景梯度变化，从而消除背景光的低频噪声。从试验效果来看，一阶和二阶差分不仅能进行边缘检测，而且能通过卷积运算消除背景光分布不均，磨粒边缘有点扩大，有些少量信息损失掉了，这种损失可以用图象形态学中的腐蚀的方法消除。两种方法相比，采用背景补偿的方法效果更好一些，在条件许可的情况下尽量采用背景光补偿方法：     

融合分数阶微分边缘特征的自适应跟踪

为提高跟踪方法对背景信息、光照变化的抗干扰能力,提出融合分数阶微分边缘特征信息的改进跟踪方法。将R-L分数阶微分边缘检测算子与Laplacian边缘检测算子进行融合,构造出对高、中频信息提升,而对低频信息能够非线性保留的混合边缘检测算子,并利用其实现目标模板及场景图像的边缘特征信息检测。基于目标色度特征和边缘特征两种直方图模型,分别建立场景图像的反向概率投影值,根据背景信息的动态变化,以抑制背景干扰信息为目的,建立自适应融合的反向概率投影图,提高算法对不确定环境变化信息的鲁棒性。实验结果表明,混合边缘检测算子能够提高边缘图像的信噪比,改善边缘提取的效果。边缘、色度特征信息自适应融合的跟踪方法单帧跟踪时间小于20 ms,满足实时性要求。该方法能够在光照变化明显和与目标颜色相似背景等情况下有效实现目标识别与跟踪功能。     

输电线路巡检智能单兵装备全景采集系统设计

为了提升图像采集效果和抗干扰能力,结合单人背包式采集方式,设计输电线路巡检中的单人背包式全景采集系统.该系统由后台管理模块和单人背包式采集设备等组成,以 GPS 卫星定位技术定位全景采集设备,以互联网方式连接 GPRS 基站和后台管理模块,实现了输电线路巡检任务从云端到终端的执行过程;单人背包式采集设备利用数码相机、云台、红外仪器采集线路图像,并以 USB 串口方式传输到后台管理模块;后台管理模块通过 SIFT 图像特征提取后,拼接全景图像,实现全景图像采集.实验结果表明,该系统具有图像采集角度广、图像采集能力强、信息传输不受噪声干扰等优点.     

图像颜色特征提取在铁谱图像分类及磨粒识别中的应用研究

铁谱图像所包含的彩色信息对磨粒识别、磨损形式分析非常重要。对图像分类技术进行研究，主要对铁谱图像的颜色特征进行研究，提出了聚类树分析、模糊聚类技术与统计分析相结合的定量研究方法，对铁谱图像进行背景、磨粒区域分割，以获得可进行定量分析的磨粒。计算的颜色特征为铁谱图像的进一步处理和识别以及磨粒的机器自动识别、磨损形式分析奠定了基础。     

全局参数估计的水下目标偏振复原方法

水体介质对光的吸收和散射是导致水下成像退化的两大主要影响因素。针对水下目标成像,本文提出了一种全局参数估计的水下目标偏振复原方法,利用构建的精简水下目标偏振重构模型,通过自动估计全局最优偏振信息重构参数,复原出水下目标图像,降低水体对图像质量的影响。在估计重构参数的过程中,首先,采用偏振中值滤波方法和基于亮原色原理的方法,分别估算水下背景光偏振度信息和无穷远处水下背景光强值;再利用基于最小互信息原则对估计的背景光偏振度信息进行优化;然后采用水下目标偏振图像增强算法将得到的水下目标重构图进行细节增强处理,最终获得复原后的水下目标辐射信息图。实验结果表明,在性能评价指标方面,相较于水下原图和其他水下复原方法处理图,利用本文方法处理后所得的图像增强测量值EME平均提高了120%,图像质量得到了明显的改善。该方法解决了人工取景估计参数不佳的问题,提高了复原目标图像的对比度,可以用于浑浊水下的目标探测与识别。     

基于结构特征的路面裂纹病害检测算法

提出一种新颖的图像分解算法,用于复杂背景下的路面病害图像,可以获得较为清晰的裂纹病害信息。通过对含有局部线性特征的裂纹图像进行形态分析,建立与结构特征相匹配的基底,利用局部Ridgelet函数作为稀疏表示的基底,图像中的结构信息作为一种组成成分被提取出来,同时运用全变差正则化方法对提取的结构信息加以正则化约束,使得提取结果更加精细化。实验结果表明利用该算法提取的裂纹病害信息符合人眼的观测结果。     

引入梯度分布特征的图像背景杂波度量

为提高图像背景杂波度量法对目标获取性能的预测精度,本文基于人眼视觉对物体边缘敏感的视觉特性,将区域梯度分布作为新的结构特征,提出了引入梯度分布特征的图像背景杂波度量法。首先,采用梯度方向直方图表征目标结构特征,选用巴氏系数度量图像目标和背景杂波在两个梯度方向直方图的相似性;然后,将基于图像结构相似性度量方法得到的结构相似性信息进行加权;最后采用D.L.Wilson提出的目标获取性能模型作为目标探测概率、虚警概率和搜索时间的预测模型对Search_2数据库中的目标进行了获取性能预测。结果显示,提出的图像杂波度量法提高了目标获取性能模型的预测精度,得到的线性相关系数分别为0.870、0.845、0.897,均方根误差分别为0.0569、0.0469、2.129,与实际观察者获得的一致性较高,且没有明显的野点,预测性能明显优于现有其他杂波度量方法。     

模拟视觉机制的隐蔽目标识别算法研究

针对从复杂图像背景中识别隐蔽目标的问题.模拟动物视觉高效的图像信息处理机制,提出一种仅用少样本快速准确地从复杂图像背景中识别出隐蔽目标的算法.首先采用拓扑ICA方法从自然图像中学习初级视皮层中复杂细胞感受野;然后用含有隐蔽目标的图像及其待配准图像分别对神经元进行刺激,筛选出对刺激有强烈响应的神经元对应内容,并通过对比找出隐蔽目标对应特征,以此对隐蔽目标进行识别.用多幅自然图像进行实验,结果表明该算法耗时少、识别率高,达到了预期的效果.     

基于模糊集理论的医学CR图像增强

数字化X光影像可以划分为目标区和背景区两部分,进行医疗诊断的信息主要集中在目标区,因此在图像进行处理时应合理地区分两部分并采用不同的方法进行处理.本文引入模糊集的概念来描述目标区和背景区,并测定了隶属度函数,建立了基于模糊集理论的图像处理模型,给出了具体实现方法.处理后的图像增强了目标区图象的视觉效果,使医学信息得到了更好的表达,进而提高诊断的准确性.     

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/ .     

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。     

基于改进双边滤波的气体泄漏检测

针对传统泄漏检测方法效率低、鲁棒性差的问题,提出了一种基于红外图像技术的气体泄漏检测方法。该方法根据被测容器充气前后的红外图像的信息差异,对获得的热像图进行背景估计、差分,以及目标与噪声的辨别,利用基于改进的双边滤波算法实现对泄漏点的检测与定位。实验证明：双边滤波算法对背景的预测具有鲁棒性,通过目标与噪声的扩散性区别分辨出目标,实现对泄漏点的准确定位。     

重轨图像增强与边缘提取的关键技术

针对重轨图像两个边缘像素特征不一致,传统边缘算子检测法难以精确提取边缘的问题,提出了一种新的边缘提取方法.该方法利用灰度强对比度拉伸算法对重轨表面和背景进行差异化拉伸增强边缘信息,削弱背景信息.运用最大方差比算法选取增强后图像的最佳阈值实现二值化.最后,运用递归连通域标识法定位边缘像素坐标,完成图像分割.对随机选取的30幅图像进行分析表明:处理后的图像边缘灰度特征明显增强,有效地抑制了表面纹理及虚假边缘.重轨表面像素宽度波动减少到-0.64％～0.34％.离散预处理算法通过遍历寄存器全局数组,减少分割时间至10.165 s.该方法在抗干扰性、准确性及时效性等方面优于传统边缘算子检测法,适用于在线工业检测系统.     

钢轨磨损视觉测量的轮廓精确快速提取

为保证钢轨磨损动态视觉测量的高精度,综合图像获取和图像处理技术,实现了清晰光条图像获取和光条中心点亚像素坐标精确提取。根据光条与背景环境亮度的高对比度,提出一种依据光条亮度的相机自动曝光法,用于获取清晰的光条图像;分析图像光条法线方向的亮度衰变特征,采用动态阈值分割法初步提取光条,滤除图像背景的同时保留光条法线方向的亮度衰减信息;根据图像过度曝光信息确定光条中心点像素大致位置,再对分割的光条图像相对应像素位置点计算Hessian矩阵,获取光条中心点的亚像素坐标。采用MFC编写应用程序进行试验,在不同光照环境和背景物的干扰下,该方法可精确地提取光条中心,与经典Steger算法相比提取精度偏差为0.05pixel,运算时间节约40%。试验结果验证了该方法稳定性好,有较强的抗干扰能力,较好地满足钢轨磨损测量的现场要求。     

应用红外图像识别技术的电机滑模变结构同步控制方法

为了使电机机械部件按照标准要求运行、满足自动化生产需求,提出一种应用红外图像识别技术的电机滑模变结构同步控制方法。利用光学系统、扫描器、探测器和制冷机等设备,组成热成像系统,采集目标具体信息,通过空间域增强和灰度均衡操作,增强图像整体画质和均匀灰度值,利用 Laplacian 算子锐化图像边缘,丰富细节信息,保护图像背景,将静止坐标系变换为旋转坐标系,结合电机的电压、定子磁链与转矩方程,构建电机运动数学模型;针对滑膜同步控制器,预留图像传输接口,将图像信息转变为电信号,选取切换函数,设置函数切换、状态变量和等效 3 种控制规律,结合不同目标实际情况,完成滑膜变结构同步控制。仿真结果表明,该算法能够获得更多目标信息,提供丰富的控制依据,提高了电机滑模变结构同步控制过程的精准度和稳定性。