共查询到10条相似文献,搜索用时 250 毫秒
1.
Background: High content screening (HCS) via automated fluorescence microscopy is a powerful technology for generating cellular images that are rich in phenotypic information. RNA interference is a revolutionary approach for silencing gene expression and has become an important method for studying genes through RNA interference‐induced cellular phenotype analysis. The convergence of the two technologies has led to large‐scale, image‐based studies of cellular phenotypes under systematic perturbations of RNA interference. However, existing high content screening image analysis tools are inadequate to extract content regarding cell morphology from the complex images, thus they limit the potential of genome‐wide RNA interference high content screening screening for simple marker readouts. In particular, over‐segmentation is one of the persistent problems of cell segmentation; this paper describes a new method to alleviate this problem. Methods: To solve the issue of over‐segmentation, we propose a novel feedback system with a hybrid model for automated cell segmentation of images from high content screening. A Hybrid learning model is developed based on three scoring models to capture specific characteristics of over‐segmented cells. Dead nuclei are also removed through a statistical model. Results: Experimental validation showed that the proposed method had 93.7% sensitivity and 94.23% specificity. When applied to a set of images of F‐actin‐stained Drosophila cells, 91.3% of over‐segmented cells were detected and only 2.8% were under‐segmented. Conclusions: The proposed feedback system significantly reduces over‐segmentation of cell bodies caused by over‐segmented nuclei, dead nuclei, and dividing cells. This system can be used in the automated analysis system of high content screening images. 相似文献
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C. J. Cornelisse A. M. J. van Driel-Kulker F. Meyer J. S. Ploem 《Journal of microscopy》1985,137(1):101-110
In order to develop an objective grading system for nuclear atypia in breast cancer, an image analysis technique has been applied for the automated recognition of enlarged and hyperchromatic nuclei in cytology specimens. The image segmentation algorithm, based on the ‘top hat’ image transformation developed in mathematical morphology, is implemented on the LEYTAS automated microscope system. The performance of the segmentation algorithm has been evaluated for fifty malignant and eighty-five benign breast lesions by visual inspection of the displayed ‘flagged’ objects. The mean number of flagged objects per 1600 image fields for breast cancers was 887 (range 0–7920) of which 87% consisted of single, atypical nuclei. For benign lesions the mean number was 30 (range 0–307) of which 20% were single nuclei. By adaptation of the ‘top hat’ parameter values, a more extreme subpopulation of atypical nuclei could be discriminated. The large interspecimen variation in the breast cancer results was related to differences in DNA content distribution and mean nuclear area, determined independently with scanning cytophotometry, and to some extent with the histological type. 相似文献
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乳腺癌已经成为女性最常见的恶性肿瘤,组织切片显微图像的病理分析是诊断的主要手段,细胞的准确分割是病理分析的重要环节。该文提出了一种新的乳腺细胞显微图像的自动分割算法:首先结合小波分解和多尺度区域生长算法分离细胞和背景,实现对细胞的精确定位;然后采用改进的数学形态学对粘连细胞进行一次细分割;接着再采用基于曲率尺度空间(CSS)的角点检测分割算法对粘连细胞进行二次细分割;两次细分割方法构成了一个双策略去粘连模型,保证了去粘连的准确性和鲁棒性。将算法应用到22幅乳腺细胞显微图像上,可以对不同类型的乳腺细胞图像进行全自动分割,有较高的分割灵敏度(0.944±0.024)和特异度(0.937±0.038),且具有较好的普适性。 相似文献
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C. ORTIZ DE SOLÓRZANO E. GARCÍA RODRIGUEZ A. JONES D. PINKEL J. W. GRAY D. SUDAR & S. J. LOCKETT 《Journal of microscopy》1999,193(3):212-226
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. 相似文献
5.
In traditional cancer diagnosis, (histo)pathological images of biopsy samples are visually analysed by pathologists. However, this judgment is subjective and leads to variability among pathologists. Digital scanners may enable automated objective assessment, improved quality and reduced throughput time. Nucleus detection is seen as the corner stone for a range of applications in automated assessment of (histo)pathological images. In this paper, we propose an efficient nucleus detector designed with machine learning. We applied colour deconvolution to reconstruct each applied stain. Next, we constructed a large feature set and modified AdaBoost to create two detectors, focused on different characteristics in appearance of nuclei. The proposed modification of AdaBoost enables inclusion of the computational cost of each feature during selection, thus improving the computational efficiency of the resulting detectors. The outputs of the two detectors are merged by a globally optimal active contour algorithm to refine the border of the detected nuclei. With a detection rate of 95% (on average 58 incorrectly found objects per field‐of‐view) based on 51 field‐of‐view images of Her2 immunohistochemistry stained breast tissue and a complete analysis in 1 s per field‐of‐view, our nucleus detector shows good performance and could enable a range of applications in automated assessment of (histo)pathological images. 相似文献
6.
针对当前多级模糊熵算法在分割人体红外图像时,存在划分数需人工指定,全局划分导致熵的信息度量精度受背景干扰,分割精度不高等问题,提出了非监督层次化模糊相关分割。首先采用熵率法将图像划分为若干超像素,确保区域一致性,提高后续处理效率;随后,用准确度量划分适当性的模糊相关来描述图像,构建模糊相关图割2-划分算子,提高层次化分割中单步分割的精度。2-划分算子的核心思想是利用提出的递推计算策略,快速搜索最大模糊相关时目标和背景的划分概率,并用其来设置图割的数据项,实施超像素的模糊相关图割2-划分。最后将2-划分算子与自顶向下的非监督层次化分割策略相结合,迭代地对目标超像素区域实施2-划分,自适应确定划分数,获得人体目标。实验结果表明:较常用算法,该算法不但能自动确定划分数,而且分割精度还提高了约18%,运行时间约为3.8s,能有效用于人体红外图像分割的工程实践中。 相似文献
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基于形态特征判别超声图像中乳腺肿瘤的良恶性 总被引:3,自引:0,他引:3
乳腺肿瘤超声图像的形态特征对判别肿瘤的良恶性具有重要的价值。为提高乳腺肿瘤超声诊断的准确率,提出一种基于其形态特征进行分类判别的计算机辅助诊断系统。该系统首先采用灰度阈值分割和动态规划相结合的方法提取超声图像中乳腺肿瘤的边缘,然后对所得边缘计算相应的三种形态参数,最后分别采用Fisher线性判据、误差反向传播神经网络和径向基函数神经网络对形态参数进行分类。该系统在157幅乳腺肿瘤(包括良性81例、恶性76例)超声图像上训练和测试,三种分类器均能取得较高的判别精度,其中误差反向传播神经网络和径向基函数神经网络的判别准确率、敏感性和特异性分别高达94.95 %、95.74%和94.23%。结果表明,基于乳腺肿瘤超声图像的形态特征建立的神经网络系统对肿瘤的良恶性具有较好的判别能力。 相似文献
9.
An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images 下载免费PDF全文
Yanen Guo Xiaoyin Xu Yuanyuan Wang Yaming Wang Shunren Xia Zhong Yang 《Microscopy research and technique》2014,77(8):547-559
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. 相似文献
10.
Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections 总被引:3,自引:0,他引:3
C. WÄHLBY I.-M. SINTORN F. ERLANDSSON G. BORGEFORS & E. BENGTSSON 《Journal of microscopy》2004,215(1):67-76
We present a region‐based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over‐segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two‐ as well as three‐dimensional images. 相似文献