In　view　of　the　problem　that　the　fixed　scale　active　contour　model　cannot　quickly　and　accurately　segment　images　with　intensity　inhomogeneity,　an　adaptive　scale　active　contour　model　based　on　the　information　entropy　is　proposed　for　image　segmentation.　Firstly,　we　put　forward　a　novel　energy　function　by　using　Maximum　Posterior　Probability　(MAP)　and　Bayes　classification　criterion,　which　greatly　improve　the　ability　to　extract　the　image　intensity　information　and　the　segmentation　accuracy　for　inhomogeneous　images.　Secondly,　we　construct　an　adaptive　scale　operator　by　using　the　image　information　entropy　to　let　the　model　can　automatically　adjust　the　scale　according　to　the　intensity　inhomogeneity　degree　of　the　image,　which　improves　the　segmentation　speed　of　the　model.　Finally,　in　order　to　verify　the　superiority　of　our　model,　we　make　a　comparison　between　our　model　and　LGDF　model,　and　also　make　an　objective　and　quantitative　analysis　of　the　segmentation　results　by　using　the　segmentation　time,　the　number　of　iterations　and　the　similarity　index.　The　final　results　show　that　the　proposed　model　not　only　has　high　robustness　to　the　initial　contour,　but　also　has　high　accuracy　and　efficiency　in　segmenting　images　with　intensity　inhomogeneity.　©　2017,　Editorial　Board　of　Journal　of　Northwestern　Polytechnical　University.　All　right　reserved.