Splitting touching cells based on concave points and ellipse fitting

A new touching cells splitting algorithm based on concave points and ellipse fitting is proposed in this paper. The algorithm includes two parts: contour pre-processing and ellipse processing. The purpose of contour pre-processing is to smooth fluctuations of the contour, find concave points of the contour and divide the contour into different segments via the concave points. The purpose of ellipse processing is to process the different segments of the contour into possible single cells by using the properties of the fitted ellipses. Because concave points divide the whole contour of touching cells into different segments and different segments of one single cell have similar properties, the ellipse processing can separate the touching cells through ellipse fitting. This paper demonstrates a new way of using ellipse fitting to split the binary contour of touching cells. Experimental results show that our algorithm is efficient.     

一种改进的桥梁图像分水岭分割算法

针对传统分水岭分割方法存在的过分割问题,提出了一种改进的桥梁图像分水岭分割算法。该算法首先对桥梁裂缝图像进行高低帽形态学滤波,并运用多尺度梯度算子提取梯度图像,在分水岭变换之前使用自适应的标记提取方法对区域极小值进行标定,然后对初步分水岭分割的过分割区域使用改进fisher距离的区域合并算法进行合并,取散度作为停止度量。实验表明,该算法减少了分水岭算法的过分割现象,提高了桥梁图像分割的精确性,具有很好的鲁棒性和适应性。     

血液红细胞图像自适应标记分水岭分割算法

目的 显微细胞的精确分割是计算机辅助诊断的前提和关键,为精确分割含有粘连重叠红细胞及病变红细胞的彩色显微图像,基于HSI模型,提出了一种自适应标记分水岭分割算法。方法 首先结合红细胞无核特点提取反光细胞的中心,从图像的S与I分量的梯度图中提取图像前景低频成分的局部极值点,两部分相结合作为初始标记,标记细胞前景;然后根据标记特点去除伪标记点,以确保所有粘连细胞的重叠区域不被标记;接着采用主成分分析从S与I分量梯度图中提取梯度信息重构梯度图,最后结合背景标记,应用标记分水岭变换实现分割。结果 选取美国社会血液学数据库中含病变和粘连的红细胞图像进行分割实验,采用平均欠分割率、平均过分割率、平均准确率3个指标对分割结果进行评价。本文算法的欠分割率为2.23%,过分割率为1.67%,均明显低于文献中两种现有分水岭算法;分割精度高达96.10%,准确度高;平均运行时间6.06 s,可保证一定的时效性。结论 提出了一种对病变粘连红细胞彩色图像的分割算法,利用饱和度与亮度信息,自适应地标记出前景细胞,提高了分割精度;采用主成分分析法,更好地保留了重叠细胞的原有边界。算法具有较好的鲁棒性,可广泛用于包括血液红细胞在内的含有类圆形的重叠、粘连细胞的显微染色图像的分割。     

基于数学形态学的灰度图像连接物体分割方法

由于噪声的存在以及连接物体的特点,传统的标记分水岭算法对包含连接物体的灰度图像很难取得满意的分割结果;特别是在背景并不连通的情况下,误分割更为常见;在标记分水岭算法的基础上,提出了一种连接物体分割方法;将属于鲁棒统计的Hough变换用于提取物体标记扩展了标记分水岭算法的应用范围;针对在分割连接物体时,由于背景并非连通,因此允许背景被分别标记,并通过一个后续滤波步骤用以剔除分割后图像中的背景部分,从而得到精确的分割图像;试验证明该算法运算速度快,鲁棒性好,具有广泛的应用价值.     

On the Decomposition of Cell Clusters

Successful segmentation of a multilevel to a bilevel microscopic cell image rather frequently gives rise to touching objects which need to be separated in order to perform object specific measurements. The standard approach of dealing with this problem is a watershed decomposition of gradient, distance or low pass filtered transforms. However, if cell clustering is excessive, the cell size varies and cells have various shapes that are different from circles the watershed approaches produce unsatisfying results. We found a technique that splits cell clumps into meaningful parts. Since this method is based on the analysis of contour curvature on the scale space of Fourier coefficients relevant dominant points can be recognized. Based on an optimized heuristic approach pairs of these dominant points are recursively matched since splitted objects do not possess concavities respectively intrusions anymore. The advantages of this approach are (i) the independence of cell shapes which are clumped, (ii) the consideration of holes or background intensities within objects, (iii) the robustness in terms of convergence and a few parameters only to adapt to other families of decomposition problems. The objective of this contribution is to explain the algorithm, show its results using different examples from benchmark databases, self generated images and complex configurations of cell images.

粘连棒材图像自动分割计数技术

针对棒材图像自动计数存在的问题,提出了一种粘连棒材图像自动分割计数技术。首先采用粒度测量术估计棒材图像尺寸半径分布,自适应地选取处理参数。然后提出了一种新颖的棒材中心区域标记方法避免了初始过分割标记,在此基础上又提出了强、弱粘连概念。根据强弱粘连的特点,提出两步标记策略。分类粘连图像、获取图像的初始标记。对强粘连形成的欠分割标记区域采用逐次腐蚀算法,结合棒材形状面积等知识进行识别,获得正确的图像标记。最后融合两步图像中心区域标记结果,采用基于标记的分水岭分割算法进行图像分割。实验证明,这种方法能准确分割粘连棒材图像,实现可靠的棒材计数。     

基于改进A*算法和动态窗口法的机器人路径规划

针对移动机器人在复杂环境下(包含静态和动态环境)的路径规划效率低的问题,提出了一种改进的A*算法与动态窗口法相结合的混合算法。针对传统A*算法安全性不足的问题,采用障碍规避策略,优化节点的选择方式,增加路径的安全性;针对转折点多的问题,采用递归二分法优化策略,去除冗余节点,减少转弯次数;针对静态环境下路径平滑性不足的问题,采用动态内切圆平滑策略将折线角优化成弧度角,以增加路径的平滑性。对于传统动态窗口法的目标点附近存在障碍物时规划效果不好和容易在凹型槽类障碍物中陷入局部最优的问题,在原有的评价函数中引入了距离偏差和轨迹偏差。最后,对所提的改进A*算法和混合算法分别在静态和动态环境下与其他算法进行仿真比较。从结果可以看出,与传统混合算法相比,临时障碍环境下,路径长度和运行时间分别缩短了13.2%和65.8%;移动障碍环境下,路径长度和运行时间分别缩短了13.9%和44.9%,所提的算法提高了在复杂环境中规划路径的效率。     

分割相碰的白血球与红血球的一种新方法

本文介绍了一种分割相碰的白血球与红血球的新方法.步骤如下:首先检出没有和红血 球相碰的白血球的那部分轮廓线段,然后找出轮廓线段的端点,并按一定规律把每两个端点配 对,最后用直线或椭圆曲线把每对端点连起来,形成闭合的白血球轮廓.用这种方法成功地分 割了一千多个与红血球相碰的白血球.     

基于凹点检测的卵母细胞极体识别方法

为解决卵母细胞极体不明显时无法有效识别的问题,提出一种基于凹点检测的极体识别方法.结合Otsu算法和形态学操作提取细胞轮廓;通过角点检测和圆形掩膜方法搜索轮廓上的深凹点,设计自适应确定掩膜半径方法和凹凸特征参数判别深浅凹点,筛选出卵母细胞与极体粘连形成的深凹点,确定识别结果.实验结果表明,该方法在极体不明显时识别准确率...     

基于改进分水岭-凹点分割的矿石粒径分级检测方法

为了提高混凝土行业的生产质量，需要对矿石大小做粒径分析，传统方法是采用人工筛分处理，过程中需要耗费大量的人力物力，同时，也存在检测时间长和检测精度低等问题；针对这一难题，通过利用计算机视觉技术，提出了一种基于改进分水岭-凹点分割的矿石粒径分级检测新方法；首先，利用图像自适应中值滤波和改进的多尺度形态学处理，提取矿石轮廓特征；其次，采用改进的分水岭分割和凹点分割相结合，获得矿石之间粘连形成的深凹点集合；最后，引入反向链码模板对凹点集进行有效的分离，从而对矿石粒径做出精准的统计分析；实验结果表明，该算法的粒径分级与人工筛分的粒径分级相比较，两者之间的累积误差率在5%以内，具有较高的准确性与实用性，值得大力的推广与应用。     

有效的染色体分割新方法

在染色体图像分析与识别中,将粘连或是交叠的染色体分割开的关键技术是找到正确的分割点。通过使用一种边界链码的计算方法来准确定位分割点所属的凹点,即候选分割点;再利用候选分割点间的距离阈值和边界弧长阈值判断并筛选出正确的分割点。同时提出了两条粘连的染色体在其端部粘连或首尾粘连情况下的正确分割方法。     

基于Order-Aware网络内点筛选网络的电力巡线航拍图像拼接

电力巡线图像纹理复杂且具有视差变化,针对传统算法获取成对匹配点数量较少、配准精度较低,严重影响电力巡线无人机图像拼接效果等问题,提出了一种基于改进OANet的图像拼接算法。首先,借助加速“风”（AKAZE）算法对待拼接电力巡线图像进行粗匹配;其次,对OANet中Order-Aware模块添加挤压和激励网络（SENet）,从而增强网络对局部和全局上下文信息的抓取能力,得到更精确的成对匹配点;然后,通过MPA算法配准待拼接图像;最后,借助内容压缩感知算法计算重叠区域的最佳缝合线以完成图像拼接。改进OANet相较原OANet的正确匹配点数量增加了10%左右,耗时平均增加了10 ms;与APAP算法、AANAP算法、MPA算法等配准拼接算法相比,所提算法的拼接质量最好,其待拼接图像的重叠区域的均方根误差为0,非重叠区域未发生畸变。实验结果表明,所提算法可快速、稳定地拼接电力巡线航拍图像。     

一种改进的重叠细胞分离算法

在细胞图像识别中,经常会遇到细胞重叠的现象,需要有一种有效的方法将重叠在一起的细胞分离成单个的细胞。目前很多分离算法都是基于数学形态学的,但这些算法都存在着一定的局限性。为此,提出了一种改进的基于数学形态学的分离算法,该算法首先采用一种简单、快速的边界剥离方法得到距离变换结果,然后再用分水岭方法对细胞图像进行分离。实验结果表明,算法获得了较好的分离效果。     

优化分配策略的密度峰值聚类算法

针对密度峰值聚类算法在面对复杂结构数据集时容易出现分配错误的问题,提出一种优化分配策略的密度峰值聚类算法(ODPC)。新算法首先引入参数积γ,扩大了聚类中心的选取范围;然后使用改进的数据点分配策略,对数据集的数据点进行基于相似度指标MS的重新分配,进一步优化了簇类中点集的分配;最后使用dc近邻法优化识别数据集的噪声点。在人工数据集及UCI真实数据集上的实验均可证明,新算法能够在优化噪声识别的同时,提高复杂流形数据集中数据点分配的正确率,并取得比DPC算法、DenPEHC算法、GDPC算法更好的聚类效果。     

A multiscale gradient algorithm for image segmentation using watershelds

Watershed transformation is a powerful tool for image segmentation. However, the effectiveness of the image segmentation methods based on watershed transformation is limited by the quality of the gradient image used in the methods. In this paper we present a multiscale algorithm for computing gradient images, with effective handling of both step and blurred edges. We also present an algorithm for eliminating irrelevant minima in the resulting gradient images. Experimental results indicate that watershed transformation with the algorithms proposed in this paper produces meaningful segmentations, even without a region merging step. The proposed algorithms can efficiently improve segmentation accuracy and significantly reduce the computational cost of watershed-based image segmentation methods.     

Morphological multiscale decomposition of connected regions with emphasis on cell clusters

After binarization of cells in complex cytological and histological images the segmented structures can be rather far away from a final quantification of features of single cells since cells may overlap and cluster strongly. Separating optically, partially or totally fused entities like cells is a problem which frequently cannot be solved by a watershed segmentation or a basic morphological processing of images. However, considering different morphological scales after iterative erosion gives rise to dominant markers of singular objects. Performing a reconstruction by iterative dilation yields a scale-independent decomposition of multiple disjointed cell clumps of different sizes within an image.Accordingly we developed a technique that splits cell clumps into meaningful parts. Since this method is based on the analysis of the morphological-scale space, generated by iterative erosion, it is independent on the size of cell clusters. The detection of dominant points within the eroded scales are cell-specific markers. The converse integration of markers at different scales is obtained by a successive reconstruction based on constrained dilation of the original cell shape.The advantages of this approach are the independence of cell shapes which are clumped, the consideration of holes or background intensities within objects and the robustness with regard to convergence. An important benefit is the fitting of the operation time to the size of clusters by the size of the morphological structuring element. This means, that this approach requires only one parameter. Finally, a better match of the morphological scale space approach was found and compared with the ground truth as well as the results of the watershed technique.The primary object of this paper is to highlight the algorithm and its results by using different examples from benchmark databases, self generated images that exhibit different topological features and complex configurations of cells within histological images.     

Radial symmetries based decomposition of cell clusters in binary and gray level images

The segmentation of structures of complex cytological and histological images is a necessary intermediate step for image analysis that give rise to binary images. In many cases these binary images can be rather far away from a subsequent object specific quantification because biological structures digitized by optoelectronic devices may situated close together so that they appear as one fused object in the projective image. Such fusions of objects may become complex so that large clusters of biological structures emerge. To quantify individual objects of a cluster they must be separated. The shape, size and intensity variation of cells in complex organs like the brain may breed planar configurations that can be splitted only inadequate by common techniques, e.g., watershed separation or basic morphological processing of images.Considering iteratively object contours suitable features of saliency can be accumulated that give rise to markers of singular objects. Such significant markers may drive a separation process more effective than common approaches. The determination of markers by an iterative method should be scale, translation and rotation invariant and robust with regard to noise due to the variability of biological specimen.We realize a technique that splits cell clumps consisting of different cell sizes and shapes into meaningful parts. The multiscale method applied here is based on the analysis of the contour shape and the object area by iterative voting using oriented kernels. These cone-shaped kernels vote iteratively for the local center of mass of the components of an aggregation. The voting is performed along the gradient of the distance transformation of the binarized image of aggregates. Iterative voting is initialized by voting along the gradient direction where at each iteration the voting direction and shape of the kernel is refined, resp. the kernel topography is refined and reoriented iteratively. It turned out that the kernel topography is unique because it votes for the most likely set of grid points where the gravity center of an individual cluster component may be located. Furthermore, a new procedure is realized to use the local intensities of aggregations for kernel voting. The last voted iteration provides gravitation centers, resp. centers of mass of the clumped cells. These are extracted and used as markers to determine individual cell boundaries by a marker based watershed postprocessing.The subject of this paper is to highlight the basic algorithm of iterative kernel voting and expanding it to process intensities within clusters as well as contour information. The approach is applied to synthetic images that were modified systematically with regard to object topology. Natural aggregates of cells at the light microscopic level and cell clusters derived from high resolution flat bed scanning were splitted. In addition to these examples images from a benchmark databases were investigated. The splittings generated by the iterative voting approach were compared with expected splittings of test persons and with results of the watershed method. Especially the gray level based iterative voting method provides superior results for cell cluster separation in comparison to the watershed procedure.     

基于分水岭和区域面积加权的粘连枸杞分级方法

针对枸杞分级过程中出现的粘连枸杞分级效率不高、准确性低的缺点,提出了一种基于形态学分水岭和区域面积加权的粘连枸杞分级方法.对粘连的枸杞图像进行形态学预处理消除枸杞烘干晾晒过程中产生的细小噪声,在保持区域轮廓位置不变的同时,尽可能地消除不规则边缘;运用标记极小值的分水岭算法分割图像,依据枸杞红色分量的分布剔除霉变颗粒,对正常枸杞二值图像运用区域面积标记算法扫描标定各个目标获取颗粒的面积,并用霍特林算法获取颗粒的长宽比;以长宽比作为面积的权值对面积加权修正后进行聚类分析,分成3类.实验结果表明:该方法能够较快速、准确地对不同大小的粘连枸杞颗粒进行分类.     

基于多特征描述的乳腺癌肿瘤病理自动分级

为了辅助病理医生快速高效诊断乳腺癌并提供乳腺癌预后信息,提出一种计算机辅助乳腺癌肿瘤病理自动分级方法。该方法使用深度卷积神经网络和滑动窗口自动检测病理图像中的细胞;随后综合运用基于稀疏非负矩阵分解的颜色分离、前景标记的分水岭算法以及椭圆拟合得到每个细胞的轮廓。基于检测到的细胞和拟合出的细胞轮廓,提取出肿瘤的组织结构特征和上皮细胞的纹理形状特征等共203维的特征,运用这些特征训练支持向量机分类器(SVM),实现对病理组织图像自动分级。17位患者的49张H&E染色的乳腺癌病理组织图像自动分级的100次十折交叉检验评估结果表明:基于病理图像的细胞形状特征与组织的空间结构特征对病理图像的高、中、低分化等级分类整体准确率为90.20%;同时对高、中、低各分化等级的区分准确率分别为92.87%、82.88%、93.61%。相比使用单一结构特征或者纹理特征的方法,所提方法具有更高的准确率,能准确地对病理组织图像中肿瘤的高级和低级分化程度自动分级,且各分级之间的准确率差异较小。     

Recognition and analysis of cell nuclear phases for high-content screening based on morphological features

Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method.