Tracking of Coronal White-Light Events by Texture

The extraction of the kinematic properties of coronal mass ejections (CMEs) from white-light coronagraph images involves a significant degree of user interaction: defining the edge of the event, separating the core from the front or from nearby unrelated structures, etc. To contribute towards a less subjective and more quantitative definition, and therefore better kinematic characterization of such events, we have developed a novel image-processing technique based on the concept of “texture of the event”. The texture is defined by the so-called gray-level co-occurrence matrix, and the technique consists of a supervised segmentation algorithm to isolate a particular region of interest based upon its similarity with a pre-specified model. Once the event is visually defined early in its evolution, it is possible to automatically track the event by applying the segmentation algorithm to the corresponding time series of coronagraph images. In this paper we describe the technique, present some examples, and show how the coronal background, the core of the event, and even the associated shock (if one exists) can be identified for different kind of CMEs detected by the LASCO and SECCHI coronagraphs.