PED-based　image　segmentation　based　on　the　active　contour　model　attracts　many　researchers　due　to　the　high　precision　of　edge　detection　and　the　continuity　of　boundaries.　Its　basic　idea　is　to　define　an　energy　functional　on　a　dynamic　curve　which　achieves　its　minimum　when　the　curve　conforms　to　the　boundary　of　the　objects.　The　most　widely　used　optimization　method　is　the　gradient-descent　method.　However,　the　convergence　of　the　gradient-descent　method　is　very　poor.　In　this　paper,　the　effectiveness　of　the　generalized　Newton　method　is　investigated　by　using　it　to　minimize　the　energy　functional　of　the　RSF&CV　model,　which　is　a　simple　combination　of　the　CV　model　and　the　RSF　model.　The　experimental　results　show　the　accuracy　and　efficiency　with　robustness　in　noise.