Automatic particle detection through efficient Hough transforms

Manual selection of single particles in images acquired using cryo-electron microscopy (cryoEM) will become a significant bottleneck when a very large number of images are required to achieve three-dimensional reconstructions at near atomic resolution. Investigation of fast, accurate approaches for automatic particle detection has become one of the current challenges in the cryoEM community. At the same time, the investigation is hampered by the fact that few benchmark particles or image datasets exist in the community. The unavailability of such data makes it difficult to evaluate newly developed algorithms and to leverage expertise from other disciplines. The paper presents our recent contribution to this effort. It also describes our newly developed computational framework for particle detection, through the application of edge detection and a sequence of ordered Hough transforms. Experimental results using keyhole limpet hemocyanin (KLH) as a model particle are very promising. In addition, it introduces a newly established web site, designed to support the investigation of automatic particle detection by providing an annotated image dataset of KLH available to the general scientific community.