The　active　contour　model　is　a　widely　used　method　for　image　segmentation.　Most　existing　active　contour　models　yield　poor　performance　when　applied　to　images　with　severe　intensity　inhomogeneity.　To　address　this　issue,　we　propose　an　adaptive-scale　active　contour　model　(ASACM)　based　on　image　entropy　and　semi-naive　Bayesian　classifier,　which　achieves　simultaneous　segmentation　and　bias　field　estimation　for　images　with　severe　intensity　inhomogeneity.　Firstly,　an　adaptive　scale　operator　is　constructed　to　adaptively　adjust　the　scale　of　the　ASACM　according　to　the　degree　of　the　intensity　inhomogeneity.　Secondly,　we　define　an　improved　bias　field　estimation　term　via　distributing　a　dependent-membership　function　for　each　pixel　to　estimate　the　bias　field　in　severe　inhomogeneous　images.　Thirdly,　a　new　penalty　term　is　proposed　using　piecewise　polynomial,　which　helps　to　avoid　time-consuming　re-initialization　process　and　instability　in　conventional　penalty　term.　The　experimental　results　demonstrate　that　the　proposed　ASACM　consistently　outperforms　many　state-of-the-art　models　in　segmentation　accuracy,　segmentation　efficiency　and　robustness　w.r.t　initialization　and　noise.　(C)　2018　Elsevier　Ltd.　All　rights　reserved.