Shearlet Analysis of Confocal Laser-Scanning Microscopy Images to Extract Morphological Features of Neurons
Shearlet Analysis of Confocal Laser-Scanning Microscopy Images to Extract Morphological Features of Neurons
复制标题
共焦激光扫描显微图像的剪切波分析以提取神经元的形态特征
DOI：
10.1007/978-1-4939-0381-8_14
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Kutyniok G
中科院分区：
文献类型：
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作者：
Sündermann F;Lotter S;Lim WQ;Golovyashkina N;Brandt R;Kutyniok G
随着激光扫描显微镜技术的进步，计算图像分析方法日益成为生物学研究的热点。图像分析的方法大致可以分为两类：第一类涉及分割和降噪问题，而第二类侧重于结构的统计和形态分析。结构分析在很大程度上依赖于分割方法的输出质量，分割方法的目的是将数字图像从不希望的(背景)和期望的(前景)信息中分离出来。显微技术的进步导致了大量包含精细结构的高度详细的图像。不幸的是，这样的图像不可能在不丢失相当多的细节和信息的情况下以自动方式被分离。在这一章中，我们提出了一种基于压缩传感方法的方法来从图像背景中分离生物相关信息，在这种情况下是树突的结构。这种方法的优点是，它可以在大图像中分离出即使是细微的结构细节，而不存在基于强度的算法的共同缺点。我们编写了一个可免费下载的软件套件，并提出了一个详细的协议，用于从荧光染色的3D图像堆栈中确定树突状树的形态特征。
Due to the progress in laser scanning microscopy techniques computational image analysis methods increasingly have come in the focus of biology. Methods of image analysis can broadly be divided into two groups: The first group deals with segmentation and noise reduction problems, while the second group focuses on the statistical and morphological analysis of structures. Structure analysis strongly depends on the quality of the output of segmentation approaches, which aim to separate the digital image in undesirable (background) and desirable (foreground) information. The progress in microscopy techniques has led to a large amount of highly detailed images containing fine structures. Unfortunately, such images cannot be separated in an automated way without loss of considerable detail and information. In this chapter, we present an approach that is based on compressed sensing methods to separate biologically relevant information, in this case the structure of dendrites, from an image background. The approach has the advantage that it allows separating even fine structural details in large images without the common disadvantages of intensity based algorithms. We have written a freely downloadable software suite and present a detailed protocol of its use to determine morphological features of dendritic trees from fluorescence stained 3D image stacks.
DOI：
10.1007/978-1-61779-536-7_24
发表时间：
2012-01-01
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
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作者：
Sundermann, Frederik;Golovyashkina, Nataliya;Bakota, Lidia
通讯作者：
Bakota, Lidia