Objective:　Intravascular　ultrasound　(IVUS)　image　segmentation　of　arterial　wall　boundaries　are　essential　not　only　for　the　quantitative　analysis　of　the　characteristics　of　vascular　walls　and　plaques　but　also　for　the　qualitative　analysis　of　vascular　elasticity　and　the　reconstruction　of　the　3D　model　of　arteries.　The　importance　lies　in　the　following:　1)IVUS　image　segmentation　is　the　basis　for　follow-up　work,　such　as　plaque　extraction　and　recognition,　vessel　wall　elasticity　analysis,　and　image　registration.　2)　Doctors　must　evaluate　the　morphological　characteristics　of　blood　vessels　and　plaques,　such　as　the　maximum　or　minimum　diameter　of　the　lumen,　cross-sectional　area,　and　plaque　area.　IVUS　provides　a　reliable　data　support　for　doctors　to　diagnose　patients　objectively.3)　IVUS　can　locate　the　region　of　interest　to　determine　the　position　and　shape　of　the　anatomical　structure　for　interventional　surgery　and　the　diagnosis　and　treatment　targets　for　radiotherapy,　chemotherapy,　and　surgery.　However,　given　the　different　environments　in　which　the　intima　and　adventitia　are　located,　traditional　segmentation　methods,　which　belong　to　serial　extraction　methods,　need　to　design　the　segmentation　algorithms　for　intima　and　adventitia　separately.　Moreover,　extremely　complex　models　affect　the　speed　of　segmentation.　To　address　these　problems,　this　paper　proposes　a　segmentation　method　based　on　the　extreme　region　detection　of　IVUS　images.Method:　The　problem　of　edge　detection　is　broadened　to　the　problem　of　extreme　region　detection,　and　the　proposed　method　consists　of　three　parts:　extreme　region　detection,　extreme　region　screening,　and　contour　fitting.　First,　edge　points　are　extracted　from　the　IVUS　image,　and　a　global　vector　is　created　by　using　the　edge　points　and　threshold　images　by　each　gray　level　to　obtain　the　gray　thresholds.　The　obtained　thresholds　make　the　change　of　threshold　images　most　stable.Next,　the　final　threshold　images　are　obtained　on　the　basis　of　the　filtered　gray　thresholds.　The　morphological　closing　operation　is　used　to　fill　in　the　small　holes　of　the　threshold　images,　and　the　connected　component　labeling　algorithm　is　used　to　mark　the　connected　regions　in　the　threshold　images　to　obtain　the　final　extreme　regions.　In　addition,　extreme　regions　contains　regions　with　unstable　states　and　large　or　small　areas　that　cannot　represent　the　lumen　and　media　because　the　extracted　extreme　regions　contain　many　sub-regions.　Therefore,　the　area　of　the　extreme　regions　must　be　screened　for　preliminary　filtering.By　using　local　binary　mode　feature,　gray　difference,　and　edge　circumference,　a　filter　vector　based　on　region　stability　is　designed　to　extract　two　extreme　regions　representing　the　lumen　and　media.　Finally,　the　contours　of　the　lumen　and　media　regions　are　fitted　by　ellipse　to　complete　the　segmentation.　Result:　Qualitative　and　quantitative　analyses　are　used　to　evaluate　the　accuracy　of　the　proposed　method.The　extreme　region　and　final　contours　are　initially　qualitatively　displayed　on　a　standard　published　dataset　containing　32　620　MHz　IVUS　B-mode　images.　The　extracted　final　contours　are　qualitatively　compared　with　the　results　drawn　manually　by　clinical　experts.The　artifacts　are　also　classified　on　the　basis　of　their　types.　For　images　without　artifacts　and　with　different　types　of　artifacts,　the　robust　performance　and　generalization　performance　of　the　proposed　algorithm　are　verified　by　calculating　the　DC　coefficient,　JI　index,　PAD　index,　and　HD　distance.　On　the　basis　of　the　DC　coefficient,　JI　index,　PAD　index,　and　HD　distance,　the　inner　border　index　values　are　0.94±0.02,　0.90±0.04,　0.05±0.05,　and　0.28±0.14　mm,　respectively;　the　outer　border　index　values　are　0.91±0.07,　0.87±0.11,　0.11±0.11,　and　0.41±0.31　mm,　respectively.In　addition,　the　values　of　DC　coefficient,　JI　index,　PAD　index,　and　HD　distance　of　the　IVUS　image　segmentation　algorithms　in　the　relevant　literature　in　the　last　few　years　are　compared　with　those　of　the　proposed　method.The　quantitative　comparison　with　the　relevant　literature　in　the　last　few　years　shows　the　improved　performance　of　the　inner　and　outer　borders　extracted　by　the　proposed　method.　In　addition,　the　test　results　of　the　proposed　method　on　the　clinical　dataset　are　very　good.Conclusion:　The　method　proposed　is　suitable　for　the　extraction　of　not　only　the　inner　border　but　also　the　outer　border;　it　is　a　parallel　extraction　algorithm.　Experiment　results　show　that　in　addition　to　high　extraction　accuracy,　the　proposed　method　has　strong　robustness　and　outperforms　several　state-of-the-art　segmentation　approaches.　©　2020,　Editorial　and　Publishing　Board　of　Journal　of　Image　and　Graphics.　All　right　reserved.