Optic　disk　(OD)　is　considered　one　of　the　main　features　of　a　retinal　image.　OD　detection　plays　an　important　role　in　retinopathy　analysis.　In　this　study,　a　new　OD　detection　method　using　saliency　model　based　on　clustering　is　presented　to　simulate　the　human　filtering　mechanism　of　visual　system　for　OD　detection　of　fundus　images.　First,　the　candidates　of　OD　regions　are　extracted　from　fundus　images　using　k-means　clustering.　Second,　two　saliencies　of　sub-regions　are　computed,　and　the　maximum　saliency　region　from　the　image　is　selected　as　the　OD　region.　Third,　the　original　OD　contour　can　be　extracted　by　ellipse　fitting　after　detecting　the　convex　hull　of　the　OD.　Finally,　the　OD　contour　can　be　accurately　segmented　by　active　contour.　A　test　is　performed　with　1422　colored　fundus　images　from　four　different　colored　fundus　image　databases.　Experimental　results　indicate　that　the　detection　accuracy　for　OD　is　up　to　94%,　and　the　segmentation　accuracy　is　up　to　88%　for　Drishti-GS　database.　The　proposed　method　effectively　overcomes　the　influence　of　large　bright　lesions　on　OD　detection　and　is　applicable　to　incomplete　ODs.　The　method　also　does　not　rely　on　vessel　segmentation,　which　results　in　short　computation　time.　This　study　also　confirms　the　effectiveness　and　robustness　of　the　proposed　algorithm.　©　2018　Computer　Society　of　the　Republic　of　China.　All　rights　reserved.