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Automatic analysis method of protein expression images based on generalized data field

机译:基于广义数据字段的蛋白质表达图像自动分析方法

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For detection of protein expression in biomedicai image, shape measurement of protein expression mostly depends on semi-automatic analysis of image analysis software which makes the results vulnerable to subjective factors, since the automatic analysis is too complicated to operate. Therefore, a novel algorithm based on generalized data field (GDF) is proposed to determine the region of protein expression. Instead of being directly divided into the measured object and background, all the data objects, namely pixels of an image, are naturally clustered into multiple classes based on potential distribution in generalized data field. Each class represents protein expression in different degree, which precisely describes the details of protein expression. Compared with image-pro plus software analysis, KM and EM, experiment results demonstrate that the protein expression can be extracted easily and objectively from an image by GDF. Furthermore, noises of background are eliminated by the smoothing procedure of GDF.
机译:在biomedicai图像检测蛋白质表达的,蛋白表达的形状测量主要取决于图像分析软件,这使得结果容易受到主观因素的半自动分析中,由于自动分析太复杂操作。因此,提出了一种基于广义数据字段(GDF)的新型算法以确定蛋白质表达的区域。而不是直接分为测量的对象和背景,所有数据对象,即图像的像素,即基于广义数据字段中的潜在分布自然地聚集到多个类别中。每个类代表不同程度的蛋白质表达,这精确地描述了蛋白质表达的细节。与Image-Pro加上软件分析,KM和EM相比,实验结果表明,可以通过GDF容易地和客观地从图像容易地和客观地提取蛋白质表达。此外,通过GDF的平滑过程消除了背景的噪声。

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