To　segment　fuzzy　and　touching　cell　images　accurately,　an　image　segmentation　algorithm　based　on　graph　theory　and　morphological　mathematics　was　proposed　according　to　the　characteristics　of　medical　cell　images.　With　proposed　algorithm,　the　images　were　smoothed　and　sharpened,　firstly.　Then,　the　improved　Minimum　Spanning　Tree　(MST)　algorithm　was　used　to　segment　the　cell　images,　in　which　the　cell　size　and　shape　information　were　added　into　MST　graph　to　avoid　the　over-segmentation.　Furthermore,　the　adherent　cells　were　split　by　combining　the　distance　mapping　and　the　skeleton　information　in　morphological　mathematics　to　solve　the　problem　of　cell　adhesion　in　the　binary　image.　Different　from　the　traditional　watershed　algorithms,　the　split　algorithm　has　no　repeat　operation.　By　experiments,　it　shows　that　the　proposed　algorithm　can　segment　fuzzy　and　touching　cell　images　well　and　can　obtain　desired　results.