Abstract: Coronal mass ejections (CMEs),which release huge quantities of matter and electromagnetic radiation into space above the sun’s surface,are considered as one of the driven sources of space weather.Coronal dimming is now viewed as the important characteristic of CME.Dimming can help understand,predict and locate the occurrence of CME.Based on the observed data from extreme ultra-violet imaging telescope (EIT) and atmospheric imaging assembly (AIA),this paper implemented the coronal dimming detection and extraction.By analyzing the statistical characteristics of the difference images related to dimming,we applied Adaboost classification algorithm into dimming detection,and then segmented the coronal dimming region.The experiment results show that the proposed algorithm can effectively detect and extract the coronal dimming areas.Our work establishes the basis for analysis of coronal dimming features.

Key words: Coronal mass ejections,Coronal dimming,Adaboost classification,Image segmentation