According　to　Bayesian　classification　criteria,　an　improved　level　set　method　for　image　segmentation　based　on　local　and　global　information　is　proposed.　Firstly,　a　local　energy　term　based　on　local　intensity　information　is　defined.　It　can　guide　the　evolving　curve　near　the　target　settled　on　the　boundaries.　Secondly,　a　global　energy　term　is　built　according　to　the　global　intensity　information,　so　as　to　accelerate　the　evolution　of　the　evolving　curve　far　away　from　the　target.　Finally,　a　unified　level　set　framework　is　proposed　which　combines　the　local　energy　term　and　global　energy　term　together　to　improve　the　efficiency　of　segmentation　and　deal　with　images　with　intensity　inhomogeneity.　Experimental　results　show　that　this　model　is　robust　to　the　position　of　initial　contour.　In　addition,　it　can　obtain　prod　satisfying　results　in　segmenting　images　with　intensity　inhomogeneity.　©　2016,　Chinese　Institute　of　Electronics.　All　right　reserved.