Automated Coronal Loop Identification using Digital Image Processing Techniques

Mathematics – Logic

Scientific paper

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Scientific paper

The results of a Master's thesis study of computer algorithms for automatic extraction and identification (i.e., collectively, "detection") of optically-thin, 3-dimensional, (solar) coronal-loop center "lines" from extreme ultraviolet and X-ray 2-dimensional images will be presented. The center lines, which can be considered to be splines, are proxies of magnetic field lines. Detecting the loops is challenging because there are no unique shapes, the loop edges are often indistinct, and because photon and detector noise heavily influence the images. Three techniques for detecting the projected magnetic field lines have been considered and will be described in the presentation. The three techniques used are (i) linear feature recognition of local patterns (related to the inertia-tensor concept), (ii) parametric space inferences via the Hough transform, and (iii) topological adaptive contours (snakes) that constrain curvature and continuity. Since coronal loop topology is dominated by the magnetic field structure, a first-order magnetic field approximation using multiple dipoles provides a priori information that has also been incorporated into the detection process. Synthesized images have been generated to benchmark the suitability of the three techniques, and the performance of the three techniques on both synthesized and solar images will be presented and numerically evaluated in the presentation. The process of automatic detection of coronal loops is important in the reconstruction of the coronal magnetic field where the derived magnetic field lines provide a boundary condition for magnetic models ( cf. , Gary (2001, Solar Phys., 203, 71) and Wiegelmann & Neukirch (2002, Solar Phys., 208, 233)). . This work was supported by NASA's Office of Space Science - Solar and Heliospheric Physics Supporting Research and Technology Program.

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