Background:　Optic　cup　is　an　important　structure　in　ophthalmologic　diagnosis　such　as　glaucoma.　Automatic　optic　cup　segmentation　is　also　a　key　issue　in　computer　aided　diagnosis　based　on　digital　fundus　image.　However,　current　methods　didn＇t　effectively　solve　the　problem　of　edge　blurring　caused　by　blood　vessels　around　the　optic　cup.　Methods:　In　this　study,　an　improved　Bertalmio-Sapiro-Caselles-Ballester　(BSCB)　model　was　proposed　to　eliminate　the　noising　induced　by　blood　vessel.　First,　morphological　operations　were　performed　to　get　the　enhanced　green　channel　image.　Then　blood　vessels　were　extracted　and　filled　by　improved　BSCB　model.　Finally,　Local　Chart-Vest　model　was　used　to　segment　the　optic　cup.　A　total　of　94　samples　which　included　32　glaucoma　fundus　images　and　62　normal　fundus　images　were　experimented.　Results:　The　evaluation　parameters　of　F-score　and　the　boundary　distance　achieved　by　the　proposed　method　against　the　results　from　experts　were　0.7955　+/-　0.0724　and　11.42　+/-　3.61,　respectively.　Average　vertical　optic　cup-to-disc　ratio　values　of　the　normal　and　glaucoma　samples　achieved　by　the　proposed　method　were　0.4369　+/-　0.1193　and　0.7156　+/-　0.0698,　which　were　also　close　to　those　by　experts.　In　addition,　39　glaucoma　images　from　the　public　dataset　RIM-ONE　were　also　used　for　methodology　evaluation.　Conclusions:　The　results　showed　that　our　proposed　method　could　overcome　the　influence　of　blood　vessels　in　some　degree　and　was　competitive　to　other　current　optic　cup　segmentation　algorithms.　This　novel　methodology　will　be　expected　to　use　in　clinic　in　the　field　of　glaucoma　early　detection.