Automatic Virus Particle Selection-The Entropy Approach - Proenca, M. D. C. M. S.; Nunes, J. F. M.; de Matos, A. P. A. (IEEE transactions on image processing, 2013)
This paper describes a fully automatic approach to locate icosahedral virus particles in transmission electron microscopy images. The initial detection of the particles takes place through automatic segmentation of the entropy-proportion image; this image is computed in particular regions of interest defined by two concentric structuring elements contained in a small overlapping window running over all the image. Morphological features help to select the candidates, as the threshold is kept low enough to avoid false negatives. The candidate points are subject to a credibility test based on features extracted from eight radial intensity profiles in each point from a texture image. A candidate is accepted if these features meet the set of acceptance conditions describing the typical intensity profiles of these kinds of particles. The set of points accepted is subjected to a last validation in a three-parameter space using a discrimination plan that is a function of the input image to separate possible outliers.