Principal Contour Extraction and Contour Classification to Detect Coronal Loops from the Solar Images

In this paper, we describe a system that determines coronal loop existence from a given Solar image region in two stages: 1) extracting principal contours from the solar image regions, 2) deciding whether the extracted contours are in a loop shape. In the first stage, we propose a principal contour extraction method that achieves 88% accuracy in extracting the desired contours from the cluttered regions. In the second stage, we analyze the extracted contours in terms of their geometric features such as linearity, elliptical features, curvature, proximity, smoothness, and corner points. To distinguish loop contours from the other forms, we train an Adaboost classifier based C4.5 decision tree by using geometric features of 150 loop contours and 250 non-loop contours. Our system achieves 85% F1-Score from 10-fold cross validation experiments.