In　order　to　solve　the　issue　that　traditional　active　contour　models　cannot　quickly,　accurately　and　robustly　segment　inhomogeneous　intensity　images,　a　hybrid　active　contour　model　combining　bias　field　estimation　and　image　segmentation　is　proposed.　Firstly,　through　fuzzy　clustering　analysis　for　images,　a　bias　field　estimation　model　with　the　fuzzy　membership　function　is　proposed,　which　improves　the　ability　to　estimate　and　extract　image　intensity.　Secondly,　an　adaptive　scaling　operator　(ASO)　is　defined　based　on　image　information　entropy,　which　improves　segmentation　efficiency　and　robustness　to　initialization　and　to　noise.　Finally,　a　hybrid　active　contour　model　is　proposed　by　incorporating　the　bias　field　estimation　model　and　the　ASO　into　an　energy　functional.　The　final　experiment　results　show　that　the　proposed　method　not　only　has　strong　robustness　to　initialization　and　noise,　but　also　has　higher　segmentation　accuracy　and　segmentation　efficiency　for　different　degrees　of　inhomogeneous　intensity　images.　©　2018,　Editorial　Office　of　Systems　Engineering　and　Electronics.　All　right　reserved.