This　paper　presents　a　novel　correntropy-based　level　set　method　(CLSM)　for　medical　image　segmentation　and　bias　field　correction.　Firstly,　we　build　a　local　bias-field-corrected　fitting　image　(LBFI)　model　in　the　level　set　formulation　by　simultaneously　using　the　bias　field　information　and　the　local　intensity　information.　Then,　a　local　bias-field-corrected　image　fitting　(LBIF)　energy　is　introduced　by　minimizing　the　difference　between　the　LBFI　and　the　input　image　in　a　neighborhood,　which　makes　it　effective　in　segmenting　images　with　intensity　inhomogeneity.　Finally,　by　incorporating　the　correntropy　criterion　into　the　LBIF　energy,　the　proposed　CLSM　can　decrease　the　weights　of　the　samples　that　are　away　from　the　intensity　means,　which　is　more　robust　to　the　effects　of　noise.　The　CLSM　is　then　integrated　with　respect　to　the　neighborhood　center　to　give　a　global　property　of　image　segmentation　and　bias　field　correction.　Extensive　experiments　on　both　synthetic　images　and　real　medical　images　are　provided　to　evaluate　our　method,　shown　significant　improvements　on　both　efficiency　and　accuracy,　as　compared　with　the　state-of-the-art　methods.