基于改进的图论最小生成树及骨架距离直方图分割细胞图像

为了解决模糊且有粘连的细胞图像的难以分割问题,本文针对医学细胞图像的特点,应用图论的思想提出了一种图论与数学形态学结合的图像分割算法.该算法先对图像进行去噪和增强,然后用改进的图论最小生成树(MST)算法对细胞图像进行初始分割.改进的主要内容是在算法中引入了细胞尺寸和形状的信息,从而在一定程度上改善了图论算法过分割的缺陷.在进一步的图像分割中,为了解决二值图像中的细胞粘连问题,结合数学形态学的骨架边界距离信息找出分裂线将粘连细胞分离.与常规的分水岭算法完全不同,该算法没有重复性的运算.一系列细胞图像的试验表明,该算法能够很好地分割模糊且有粘连的细胞图像,其结果令人满意.     

显微图像的复杂粘连细胞个数统计

显微细胞图像分析包括对生物医学图像进行识别测定,统计计数。文中对复杂的粘连严重细胞进行处理和个数统计。首先利用细胞的色度信息对图像二值化,用链码表示出轮廓。然后通过凹点分割略微粘连的细胞,再用面积法统计粘连严重细胞和单个细胞的个数。误差小于2%。结论表明对于粘连严重的细胞,该方法比传统的腐蚀算法,单纯的凹点分割有效。     

白细胞图像的自动分割算法

根据白细胞核在其饱和度分量S和绿分量G中的分布特点,构造细胞变换图像,提取细胞核,实现白细胞的初步定位.对存在细胞粘连的细胞图像,利用细胞边缘的相位角变化来检测细胞粘连区域并除之,采用线性插值法拟合边缘曲线,得到封闭的白细胞边缘轮廓,最终提取白细胞.本算法可自动分割白细胞,具有很好的分割效果.     

一种基于水平集的多尺度乳腺肿块分割方法

作为乳腺肿块检测的重要环节,肿块分割在乳腺癌的计算机辅助诊断系统中扮演着重要的角色。提出一种基于水平集的多尺度乳腺肿块分割方法。首先对乳腺图像进行高斯金字塔分解,在粗尺度图像上使用C-V模型对肿块进行粗分割,得到的粗轮廓作为细尺度图像上的初始轮廓;考虑到C-V模型在分割灰度不均匀图像时所存在的局限性,在细尺度上提出一种局部活动轮廓模型,对粗分割的结果进行局部精细化处理。另外,为了提高分割方法的自适应能力,从粗分割结果中抽取灰度、面积特征作为局部活动轮廓模型参数设定的依据。将本文方法、C-V模型以及RSF模型应用于89例肿块病灶图像时,分别获得0.236 1、0.300 4和0.373 8的平均误分率。结果表明,所提出的多尺度方法具有更高的分割精度和鲁棒性。     

基于粒子群算法的二维Otsu阈值分割法

二维Otsu方法同时考虑了图像的灰度信息和像素间的空间邻域信息,是一种有效的图像分割方法。针对二维Otsu方法计算量大的特点,采用粒子群算法来求解最优二维阈值向量。通过分别用快速递推算法和改进粒子群算法对显微细胞图像的实验表明,后者不仅能得到理想的分割效果,且相较于前者,计算量大大减少,达到了快速分割的目的,有利于提高图像处理的实时性。     

基于显微图像识别的在线润滑油中磨粒分析方法

针对现有显微图像油液磨粒监测技术的芯片结构的不足,改进微通道与外通道结合处结构,减小了微通道被阻塞的可能性,提高了检验精度。为了提高机器润滑油中金属磨粒在线图像识别的准确性,分析图像运动模糊退化模型,并以自相关函数估算模糊尺度,采用基于维纳滤波的图像恢复算法获得清晰的磨粒图像;应用图像差值与最大类间方差法(Otsu)对图像进行分割并对分割后的磨粒图像计数获得磨粒等效直径和分布。研究结果表明,该图像分割方法提高了油液磨粒的在线实时检测的准确率,其精度达95%以上。     

基于平均密度投影和平移高斯模型的肺结节检测与分割算法

针对 CT图像中肺结节与血管粘连导致分割困难的问题,提出了一种基于平均密度投影和平移高斯模型的肺结节检测与分割算法。首先通过对二维CT序列图像作平均密度投影(AIP),融合局部三维特征生成AIP图像,然后利用阈值分割和形态学方法对结节轮廓进行粗分割,最后通过建立平移高斯模型来拟合肺结节,从而实现对肺结节的精确分割。对30个血管粘连性肺结节CT图像的实验结果表明,本文算法与专业医师标记区域的面积交迭度达到91%,能够实现对粘连型肺结节的有效分割,但对于灰度较弱且体积较小的肺结节仍存在漏检的风险,需要后续进一步研究。     

基于机器视觉的车速传感器焊点检测

应用机器视觉检测技术对车速传感器焊点进行图像处理与检测,通过CCD工业相机进行图像的采集,根据图像的特点划定感兴趣区域ROI;分析椒盐噪声干扰的影响,采用中值滤波算法对焊点图像进行去噪;采用OSTU算法推导出最佳阈值并用阈值法进行图像分割;运用开运算算法去除因阈值分割生成的细小区块;调用Open e Vision软件算法函数库计算焊点区域面积;调试检测系统程序并进行实际生产试验,结果表明:检测系统对车速传感器焊点检测精确且高效。     

提升小波和分水岭算法在矿石粒度检测中的应用

为了实现矿石粒度的在线自动化检测，需要解决两个难点：矿石图像的去噪和分割。通过提出了一种新的提升小波构造方法：基于三次B样条函数的提升小波，实现了对矿石图像的去噪。对于矿石图像的分割，提出了改进的分水岭算法。矿石粒度检测的具体步骤是：首先利用基于三次B样条函数的提升小波对图像进行去噪，再利用改进的分水岭算法对矿石图像进行分割。最后利用图像的连通域性质，计算各个连通域的像素面积，再转换到实际的矿石粒度大小，从而实现对矿石粒度的检测。对比这里算法与人工筛选的结果，累积误差在3%以内，可见这里算法具有可行性和准确性。     

基于广义反向粒子群与引力搜索混合算法的多阈值图像分割

提出了基于粒子群优化(PSO)与引力搜索(GSA)混合算法(PSOGSA)的多阈值图像分割方法来解决图像阈值搜寻过程中单一优化算法局部搜索能力不强的问题。提出了图像阈值分割领域中的广义反向学习策略,在阈值寻优过程中提高群体多样性,增强了全局搜索能力;采用了全局最优解的正态变异策略,扩展了全局最优的搜索区域,避免了算法的早熟收敛。在此基础上,实现了基于广义反向粒子群与引力搜索混合算法的多阈值图像分割方法。最后,使用本方法对复杂多目标图像进行了多阈值分割实验,并与引力搜索算法和萤火虫算法进行了比较。实验结果表明,本文方法的分割精度优于引力搜索算法与萤火虫算法,其分割目标函数值在连续运行时的标准差降低了90%以上,是一种精度高、稳定性强的多阈值图像分割方法。     

An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images

With the rapid advance of three-dimensional (3D) confocal imaging technology, more and more 3D cellular images will be available. Segmentation of intact cells is a critical task in automated image analysis and quantification of cellular microscopic images. One of the major complications in the automatic segmentation of cellular images arises due to the fact that cells are often closely clustered. Several algorithms are proposed for segmenting cell clusters but most of them are 2D based. In other words, these algorithms are designed to segment 2D cell clusters from a single image. Given 2D segmentation methods developed, they can certainly be applied to each image slice with the 3D cellular volume to obtain the segmented cell clusters. Apparently, in such case, the 3D depth information with the volumetric images is not really used. Often, 3D reconstruction is conducted after the individualized segmentation to build the 3D cellular models from segmented 2D cellular contours. Such 2D native process is not appropriate as stacking of individually segmented 2D cells or nuclei do not necessarily form the correct and complete 3D cells or nuclei in 3D. This paper proposes a novel and efficient 3D cluster splitting algorithm based on concavity analysis and interslice spatial coherence. We have taken the advantage of using the 3D boundary points detected using higher order statistics as an input contour for performing the 3D cluster splitting algorithm. The idea is to separate the touching or overlapping cells or nuclei in a 3D native way. Experimental results show the efficiency of our algorithm for 3D microscopic cellular images.     

Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Imprint cytology (IC) refers to one of the most reliable, rapid and affordable techniques for breast malignancy screening; where shape variation of H&E stained nucleus is examined by the pathologists. This work aims at developing an automated and efficient segmentation algorithm by integrating Lagrange's interpolation and superpixels in order to delineate overlapped nuclei of breast cells (normal and malignant). Subsequently, a computer assisted IC tool has been designed for breast cancer (BC) screening. The proposed methodology consists of mainly three subsections: gamma correction for preprocessing, single nuclei segmentation and segmentation of overlapping nuclei. Single nuclei segmentation combines histogram‐based thresholding and morphological operations; where segmentation of overlapping nuclei includes concave point detection, Lagrange's interpolation for overlapping arc area detection and the fine segmentation of overlapped arc area by superpixels. Total 16 significant features (p < 0.05) quantifying shape and texture of nucleus were extracted, and random forest (RF) classifier was skilled for automated screening. The proposed methodology has been tested on 120 IC images (approximately 12 000 nuclei); where 98% segmentation accuracy and 99% classification accuracy were achieved. Besides, performance evaluation was studied by using Jaccard's index (= 94%), correlation coefficient (= 95%), Dice similarity coefficient (= 97%) and Hausdorff distance (= 43%). The proposed approach could offer benefit to the pathologists for confirmatory BC screening with improved accuracy and could potentially lead to a better shape understanding of malignant nuclei.     

Comparison of two automatic cell‐counting solutions for fluorescent microscopic images

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple‐to‐use cell‐counting programs that also allow users to correct the detection results. In this paper, we present the Cellcounter and Learn 123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user‐friendly interface. Although Cellcounter is based on predefined and fine‐tuned set of filters optimized on sets of chosen experiments, Learn 123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. Cellcounter also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R² < 0.9), although Cellcounter had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours to minutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, Cellcounter overlay extension also enables fast analysis of related images that would otherwise require image merging for accurate analysis, whereas Learn 123's evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings.     

High-resolution cell outline segmentation and tracking from phase-contrast microscopy images

Accurate extraction of cell outlines from microscopy images is essential for analysing the dynamics of migrating cells. Phase-contrast microscopy is one of the most common and convenient imaging modalities for observing cell motility because it does not require exogenous labelling and uses only moderate light levels with generally negligible phototoxicity effects. Automatic extraction and tracking of high-resolution cell outlines from phase-contrast images, however, is difficult due to complex and non-uniform edge intensity. We present a novel image-processing method based on refined level-set segmentation for accurate extraction of cell outlines from high-resolution phase-contrast images. The algorithm is validated on synthetic images of defined noise levels and applied to real image sequences of polarizing and persistently migrating keratocyte cells. We demonstrate that the algorithm is able to reliably reveal fine features in the cell edge dynamics.     

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.     

基于Hough检测和C-V模型的航拍绝缘子 自动协同分割方法*

绝缘子分割是通过图像处理技术实现其运行状态自动检测及故障诊断的重要前提。针对航拍图像具有背景复杂、分辨率较低、数量多和伪目标多等特点,使用传统分割方法会产生大量的用户交互导致分割效果不佳。本文把协同分割引入到绝缘子航拍图像处理中,提出一种Hough检测修复结合自动初始化轮廓C-V模型的航拍绝缘子图像协同分割方法。本方法利用航拍绝缘子图像帧之间的关系作为先验信息以达到更高的分割精度。首先对航拍图像进行去除文本预处理;然后对预处理过的图像进行Hough检测修复以处理输电线与绝缘子粘连问题并用SLIC进行超像素分割;最后利用广义霍夫变换实现C-V模型初始轮廓的选取并进行基于图像间的C-V模型的绝缘子协同分割。实验结果表明,本文分割方法的准确率明显比其他算法高,能够有效地区分目标和背景并去除杆塔、输电线等伪目标,自动化性能良好,为无人机航拍绝缘子的状态检测及故障诊断奠定基础。     

A Hybrid Method for the Segmentation of a Ferrograph Image Using Marker-Controlled Watershed and Grey Clustering

Aimed at the correct segmentation of wear particles in ferrograph images, a new method combining marker-controlled watershed and an improved grey clustering algorithm is proposed in this article. First, the marker-controlled watershed is applied to ferrograph images to efficiently obtain the initial segmentation of wear particles. Then, an improved grey clustering algorithm utilizing color characteristics and relative position information is applied to merge the oversegmented regions after the watershed segmentation. This new algorithm is tested for ferrograph images and the results are compared with those of other algorithms. The experimental results show that the proposed method is effective for the segmentation of large wear particles and fine wear debris deposited as chains on the ferrograph, and it is proven to be a practical method for segmenting wear particles quickly and accurately.     

Automated identification of neurons and their locations

Individual locations of many neuronal cell bodies (>10⁴) are needed to enable statistically significant measurements of spatial organization within the brain such as nearest‐neighbour and microcolumnarity measurements. In this paper, we introduce an Automated Neuron Recognition Algorithm (ANRA) which obtains the (x, y) location of individual neurons within digitized images of Nissl‐stained, 30 μm thick, frozen sections of the cerebral cortex of the Rhesus monkey. Identification of neurons within such Nissl‐stained sections is inherently difficult due to the variability in neuron staining, the overlap of neurons, the presence of partial or damaged neurons at tissue surfaces, and the presence of non‐neuron objects, such as glial cells, blood vessels, and random artefacts. To overcome these challenges and identify neurons, ANRA applies a combination of image segmentation and machine learning. The steps involve active contour segmentation to find outlines of potential neuron cell bodies followed by artificial neural network training using the segmentation properties (size, optical density, gyration, etc.) to distinguish between neuron and non‐neuron segmentations. ANRA positively identifies 86 ± 5% neurons with 15 ± 8% error (mean ± SD) on a wide range of Nissl‐stained images, whereas semi‐automatic methods obtain 80 ± 7%/17 ± 12%. A further advantage of ANRA is that it affords an unlimited increase in speed from semi‐automatic methods, and is computationally efficient, with the ability to recognize ～100 neurons per minute using a standard personal computer. ANRA is amenable to analysis of huge photo‐montages of Nissl‐stained tissue, thereby opening the door to fast, efficient and quantitative analysis of vast stores of archival material that exist in laboratories and research collections around the world.     

结合先验稀疏字典和空洞填充的CT图像肝脏分割

针对复杂多变的肝脏图像,提出了一种基于先验稀疏字典和空洞填充的三维肝脏图像分割方法。对腹部CT图像进行Gabor特征提取,并分别在Gabor图像和灰度图像的肝脏金标准边界上选择大小相同的图像块作为两组训练集,利用训练集得到两种查询字典及稀疏编码。将金标准图像与待分割图像配准,并将配准后的肝脏边界作为待分割图像的肝脏初始边界;在初始边界点上的十邻域内选择大小相同的两组图像块作为测试样本,利用测试样本与查询字典计算稀疏编码及重构误差,并选择重构误差最小的图像块的中心作为待分割肝脏的边界点;最后,设计一种空洞填充方法对肝脏边界进行补全和平滑处理,得到最终分割结果。利用医学图像计算和计算机辅助介入国际会议中提供的肝脏数据进行了实验验证。结果表明,该方法对肝脏分割图像具有较好的适用性和鲁棒性,并获得了较高的分割精度。其中,平均体积重叠率误差为(5.21±0.45)%,平均相对体积误差为(0.72±0.12)%,平均对称表面距离误差为(0.93±0.14)mm。     

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.