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Fast detection of generic biological particles in cryo-EM images through efficient Hough transforms

机译:通过有效的霍夫变换快速检测冷冻EM图像中的通用生物颗粒

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One of the current challenges in the analysis of a very large number of images acquired using cryo-electron microscopy is to investigate fast, accurate approaches for automatic detection of biological particles. Cross-correlation with a reference image based techniques have been developed in the past for this purpose; both computational requirements and accuracy, however, have limited their applicability to only a very small set of particles. The paper describes a new computational framework for fast, automatic particle detection, through the application of edge detection and a sequence of ordered Hough transforms. In particular, it presents how to adapt the generalized Hough transform to efficiently detect approximately rectangular shapes in a cluttered background. Preliminary results using hemocyanin as a model particle are promising.
机译:在分析使用低温电子显微镜获得的大量图像时,当前的挑战之一是研究快速,准确地自动检测生物颗粒的方法。为此,过去已经开发了与基于参考图像的技术进行互相关的技术。但是,计算要求和准确性都将它们的适用性限制在仅一小组非常小的粒子上。本文通过边缘检测和一系列有序霍夫变换的应用,描述了一种用于快速,自动粒子检测的新计算框架。特别地,它提出了如何使广义霍夫变换适应于有效地检测杂乱背景中的近似矩形形状。使用血蓝蛋白作为模型颗粒的初步结果令人鼓舞。

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