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Review of the basic image processing and segmentation techniques for biological images

机译:回顾生物图像的基本图像处理和分割技术

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High throughput screening has been used to rapidly screen for chemical compounds in a biological assay Until recently, many of the biological assays utilized simple biochemical techniques, the result of which could be interpreted in single or at most a few numerical values. That made it easy to evaluate, without bias, any unique chemical entities screened. However, with biological cells or tissue images, the information was qualitative or at best limited to simplified algorithms. Recently, it is now becoming possible to perform standardized assays and utilize complex image data to derive reproducible information which could be utilized to precisely quantify the efficacy of compounds. Much of this is possible due to the precise mathematical algorithms that are used to compute image data to derive information. This review will discuss some of the basic algorithms involving kernel operations that are commonly used and how they can be applied for any image or picture data. (C) 2006 Society for Imaging Science and Technology.
机译:高通量筛选已用于在生物测定中快速筛选化合物。直到最近,许多生物测定都利用简单的生化技术,其结果可以解释为单个或最多几个数值。这样可以很容易地评估任何筛选出的独特化学实体,而不会产生偏差。但是,对于生物细胞或组织图像,信息是定性的,或充其量仅限于简化算法。近来,现在变得有可能执行标准化的测定并利用复杂的图像数据来获得可再现的信息,该信息可用于精确地定量化合物的功效。由于用于计算图像数据以获取信息的精确数学算法,因此很多事情都是可能的。本文将讨论一些涉及到内核操作的基本算法,以及如何将其应用于任何图像或图片数据。 (C)2006年影像科学与技术学会。

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