Image　segmentation　algorithm　is　to　divide　the　images　into　several　regions　with　specific　and　unique　characteristics,　and　is　an　important　technology　to　extract　the　interested　target.　Image　segmentation　is　the　key　step　to　realize　the　research　from　general　image　processing　into　image　analysis,　and　is　vital　preprocessing　method　of　image　recognition　and　computer　vision.　We　cannot　obtain　correct　recognition　if　we　do　not　have　correct　segmentation.　Nevertheless,　the　only　basis　of　segmentation　process　is　brightness　or　color　of　pixels　in　an　image.　In　the　processing　of　computer　automatic　segmentation,　we　experience　several　problems,　such　as　uneven　illumination,　effect　of　noise,　indistinct　part　in　image,　and　shadow,　and　these　factors　may　cause　false　segmentation.　In　order　to　overcome　the　disadvantages　of　the　traditional　segmentation　algorithm,　in　this　paper,　we　propose　a　novel　segmentation　algorithm　based　on　Markov　Random　Field.　The　segmentation　algorithm　proposed　in　this　paper　is　based　on　Markov　Random　Field　Mode　and　Bayesian　theory,　and　we　determine　the　objective　function　in　image　segmentation　problem　on　the　basis　of　optimality　criterion　of　statistical　decision　and　estimation　theory.　Some　optimization　algorithms　are　used　to　obtain　the　maximum　possible　distribution　of　Markov　Random　Field　which　satisfy　these　conditions.　The　experimental　result　reflects　the　effectiveness　and　robustness　of　our　algorithm.　As　a　supplement,　we　analyze　the　development　trend　of　the　Markov　Random　Field　theory.