The coronagraph images captured by a Solar Terrestrial Relations Observatory (STEREO)Ahead/Behind (A/B) spacecraft allow tracking of a coronal mass ejection (CME) from two different viewpoints and reconstructing its propagation in three-dimensional space. The reconstruction can be done using a triangulation technique that requires a CME front edge location. There are currently no robust automatic CME front edge detection methods that can be integrated with the triangulation technique. In this paper, we propose a novel automatic method to detect the front edge of the CME using STEREO coronagraph 2 red-colored Red, Green, Blue color model images. Our method consists of two modules: preprocessing and classification. The preprocessing module decomposes each coronagraph image into its three channels and uses only the red channel image for CME segmentation. The output of the preprocessing module is a set of segmented running-difference binary images which is fed into the classification module. These images are then transformed into polar coordinates followed by CME front edge detection based on the distance that CME travels in the field of view. The proposed method was validated against a manual method using total 56 CME events, 28 from STEREO A and 28 from STEREO B, captured in the period from 1 January 2008 to 16 August 2009. The results show that the proposed method is effective for CME front edge detection. The proposed method is useful in quantitative CME processing and analysis and will be immediately applicable to assist automatic triangulation method for real-time space weather forecasting.

COR2 coronagraph images are the most commonly used data for coronal mass ejection (CME) analysis among the various types of data provided by the STEREO (Solar Terrestrial Relations Observatory) SECCHI (Sun-Earth Connection Coronal and Heliospheric Investigation) suite of instruments. The field of view (FOV) in COR2 images covers 2-15 solar radii (Rs) that allow for tracking the front edge of a CME in its initial stage to forecast the lead-time of a CME and its chances of reaching the Earth. However, estimating the lead-time of a CME using COR2 images gives a larger lead-time, which may be associated with greater uncertainty. To reduce this uncertainty, CME front edge tracking should be continued beyond the FOV of COR2 images. Therefore, heliospheric imager (HI1) data that covers 15-90 Rs FOV must be included. In this paper, we propose a novel automatic method that takes both COR2 and HI1 images into account and combine the results to track the front edges of a CME continuously. The method consists of two modules: pre-processing and tracking. The pre-processing module produces a set of segmented images, which contain the signature of a CME, for both COR2 and HI1 separately. In addition, the HI1 images are resized and padded, so that the center of the Sun is the central coordinate of the resized HI1 images. The resulting COR2 and HI1 image set is then fed into the tracking module to estimate the position angle (PA) and track the front edge of a CME. The detected front edge is then used to produce a height-time profile that is used to estimate the speed of a CME. The method was validated using 15 CME events observed in the period from January 1, 2008 to August 31, 2009. The results demonstrate that the proposed method is effective for CME front edge tracking in both COR2 and HI1 images. Using this method, the CME front edge can now be tracked automatically and continuously in a much larger range, i.e., from 2 to 90 Rs, for the first time. These improvements can greatly help in making the quantitative CME analysis more accurate and have the potential to assist in space weather forecasting.

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