Fig. 11

Micrograph clustering and single particle picking results using different cryo-EM datasets. a Apoferritin micrograph clustering image (binary mask) using AutoCryoPicker Approach [24] based Intensity-Based Clustering Algorithm (IBC) and Apoferritin dataset [27]. b Top-view (Circular) Particles Detection and Picking Results using Modified Circular Hough Transform (CHT) [24], the center of each particle illustrated by the ‘ + ’ sign and the radius of each particle by the blue circle around each particle from the Apoferritin dataset [27]. c KLH micrograph clustering image (binary mask) using AutoCryoPicker Approach [24] based Intensity-Based Clustering Algorithm (IBC) and KLH dataset [26]. d Top-view (Circular) Particles Detection and Picking Results using Modified Circular Hough Transform (CHT) [24], the center of each particle illustrated by the ‘ + ’ sign and the radius of each particle by the blue circle around each particle from the KLH dataset [26]. e KLH micrograph clustering image (binary mask) using AutoCryoPicker Approach [24] based Intensity-Based Clustering Algorithm (IBC) and KLH dataset [26]. f Top and side-view (square) Particles Detection and Picking Results using Feret diameters detection [32] and Modified Circular Hough Transform (CHT) [24] from KLH dataset [26], the center of each particle illustrated by the ‘ + ’ sign and the radius of each particle by the blue circle around each particle from the KLH dataset [26]. g Ribosome micrograph clustering image (binary mask) using SuperCryoPicker Approach [25] based super k-means clustering (SP-K-means) and Ribosome dataset [28]. h Irregular particle shape detection and picking by SP-K-means [25] on the Ribosome dataset [28]. i Β-galactosidase micrograph clustering image (binary mask) using SuperCryoPicker Approach [25] based super k-means clustering (SP-K-means) and β-galactosidase dataset [29]. j Complex particle shape detection and picking by SP-K-means on the β-galactosidase dataset [29]