Thalamic　segmentation　serves　an　important　function　in　localizing　targets　for　deep　brain　stimulation　(DBS).　However,　thalamic　nuclei　are　still　difficult　to　identify　clearly　from　structural　MRI.　In　this　study,　an　improved　algorithm　based　on　the　fuzzy　connectedness　framework　was　developed.　Three-dimensional　TI-weighted　images　in　axial　orientation　were　acquired　through　a　3D　SPGR　sequence　by　using　a　1.5　T　GE　magnetic　resonance　scanner.　Twenty-five　normal　images　were　analyzed　using　the　proposed　method,　which　involved　adaptive　fuzzy　connectedness　combined　with　confidence　connectedness　(AFCCC).　After　non-brain　tissue　removal　and　contrast　enhancement,　the　seed　point　was　selected　manually,　and　confidence　connectedness　was　used　to　perform　an　ROI　update　automatically.　Both　image　intensity　and　local　gradient　were　taken　as　image　features　in　calculating　the　fuzzy　affinity.　Moreover,　the　weight　of　the　features　could　be　automatically　adjusted.　Thalamus,　ventrointermedius　(Vim),　and　subthalamic　nucleus　were　successfully　segmented.　The　results　were　evaluated　with　rules,　such　as　similarity　degree　(SD),　union　overlap,　and　false　positive.　SD　of　thalamus　segmentation　reached　values　higher　than　85%.　The　segmentation　results　were　also　compared　with　those　achieved　by　the　region　growing　and　level　set　methods,　respectively.　Higher　SD　of　the　proposed　method,　especially　in　Vim,　was　achieved.　The　time　cost　using　AFCCC　was　low,　although　it　could　achieve　high　accuracy.　The　proposed　method　is　superior　to　the　traditional　fuzzy　connectedness　framework　and　involves　reduced　manual　intervention　in　time　saving.　(C)　2015　Elsevier　Ltd.　All　rights　reserved.