An　improved　level　set　method　for　image　segmentation　based　on　image　local　entropy　information　is　proposed,　which　combines　the　edge-based　model　and　the　region-based　model　into　a　joint　framework.　Using　image　local　entropy　information,　an　adaptive　weighting　function　is　built　firstly,　which　enables　the　evolving　curve　choose　the　evolution　direction　and　move　to　the　object　boundary,　adaptively.　A　novel　edge　indicator　function　is　proposed　based　on　the　weighting　function,　which　improves　the　ability　of　detecting　weak　boundary　and　accelerates　the　speed　of　contour　evolution.　Finally,　the　Chan-Vese　(C-V)　model　is　introduced　into　the　joint　framework　as　an　external　energy,　which　enhances　the　model　dealing　images　with　intensity　inhomogeneity.　In　the　experiments,　the　robustness　of　the　method　is　evaluated　to　initial　contours　and　noises,　and　the　ability　of　segmenting　images　with　intensity　inhomogeneity.　The　results　show　that　the　proposed　method　can　not　only　enhance　the　robustness　to　noises　and　improve　the　ability　of　segmenting　images　with　weak　boundary,　but　also　achieve　the　satisfying　results　in　segmenting　images　with　intensity　inhomogeneity,　as　compared　with　the　other　three　methods　using　objective　numerical　indicators.　©　2016,　Editorial　Office　of　Systems　Engineering　and　Electronics.　All　right　reserved.