Color　fundus　images　have　been　widely　used　in　the　diagnosis　and　screening　of　ophthalmic　diseases.　The　macular　detection　and　foveal　location　in　fundus　images　are　important　steps　in　grading　and　diagnosis　of　ophthalmic　diseases.　An　efficient　method　not　relying　on　the　optic　and　vascular　information　was　proposed　in　this　work　for　detecting　and　locating　macular　foveal.　After　a　general　analysis　on　morphological　characteristics　of　macular,　which　was　low　brightness　and　round,　the　area　of　macular　could　be　ensured　in　the　binary　images.　Then,　an　improved　A-means　clustering　method　was　proposed　on　the　basis　of　spatial　information　of　images　and　optimizes　clustering　objects　to　obtain　the　edge　information　of　macular　and　achieve　accurate　position　of　the　macula　foveal.　Experimental　tests　showed　good　performance　in　the　public　fundus　images　database.　For　the　normal　and　the　pathological　changes　of　the　fundus　images,　the　effective　location　rate　of　the　macula　was　96.　11　%　and　92.　12%　respectively,　and　the　average　accuracy　reached　93.92%.　Thus　the　proposed　method　based　on　morphological　features　and　k-means　clustering　proved　a　simple,　efficient　and　useful　tool　for　computer-aided　diagnosis　of　ocular　diseases.　©　2017　Chinese　Academy　of　Medical　Sciences.　All　Rights　Reserved.