Perceptual Organization as a Method for Detection and Selection of Filamentous Structures in Highly Noisy Images Acquired by Cryo-electron Microscopy

This paper presents our recent progress toward a fully automated system for cryo-electron microscopy that integrates instrument control, computer vision algorithms and machine learning techniques. It describes our image analysis strategies for detection and selection of filaments in highly noisy images using multi-level perceptual organization. At the signal level, we use the Canny edge detector to detect weak boundaries. Collinearity at the primitive level is employed to organize discontinuous edges into line segments with a complete description, by using the Hough transform followed by an algorithm to detect end points of line segments. At the structural level, line segments are grouped into filamentous structures by seeking parallelism and employing high-level knowledge. In addition, statistical methods are used to split two filaments if they are joined together end-to-end. The performance of the proposed approach has been tested and evaluated by applying it to high magnification images of tobacco mosaic virus.