In　this　study,　we　propose　an　algorithm　that　uses　an　improved　Minimum　Spanning　Tree　algorithm　and　a　modified　Canny　edge　detector　to　segment　images　that　contain　a　considerable　amount　of　noises.　First,　we　use　our　modified　Canny　operator　to　pre-process　an　image,　and　record　the　obtained　object　boundary　information;　then,　we　apply　the　improved　Minimum　Spanning　Tree　algorithm　to　associate　the　above　information　with　boundary　points　in　order　to　separate　edges　into　two　classes　in　the　image,　namely　the　inner　and　boundary　regions.　In　particular,　Minimum　Spanning　Tree　algorithm　is　improved　by　using　Fractional　differential　and　combining　the　functions　of　the　intra-regional　and　inter-regional　differences　with　a　function　for　edge　weights.　Based　on　the　experimental　results,　compared　with　the　other　four　exiting　algorithms,　the　new　algorithm　has　the　higher　accuracy　and　the　better　effect　for　noised　image　segmentation.　©　The　Author(s)　2019.