Sea ice edge is an important index for illustrating changes in the Arctic sea ice. This parameter is important for navigation safety and sea ice disaster warning in Chinese northern coast. Sea ice edge is often previously obtained through long-term artificial judgment or in default equivalent to a certain isoline of sea ice concentration, which neglects the influence of large floes on sea ice edge. This study aims to develop a more accurate and faster automatic method for monitoring sea ice edge compared with conventional monitoring techniques. A novel method based on morphology is proposed. Connected Component Analysis(CCA) and image closing operation are performed. The main ice region, main water region, and floes are distinguished using CCA twice. Then, some large floes are reserved and merged into the principal ice region. Finally,sea ice edge is retrieved by a changeable image closing operation with self-adaptive structural element. This method can be extensively applied to ice-water binary data, such as sea ice concentration, satellite image, and aerial image. The proposed technique is applied on the reflectance data produced from band 1 and band 2 of MODIS(Moderate-resolution Imaging Spectroradiometer). The new method is used to retrieve sea ice edge from sea ice concentration data from advanced microwave scanning radiometer for EOS data covering ten regions in the Arctic ocean. Compared with the 15% isoline of sea ice concentration in the regions covered by many large ice floes, the sea ice edge obtained by the new method is more reasonable for monitoring large-scale sea ice. This advantage is caused by the reservation and merging of slightly large floes into the principal ice region.Rapid monitoring of sea ice in the Chinese northern coast without the restriction of chosen data formatis feasible because the method is automatic and can be widely applied. Meanwhile, the dramatic change in the Arctic sea ice can be quantified using the sea ice edge obtained by the proposed method. A regional and integral Arctic sea ice edge dataset can be built for further Arctic studies.