Fatty　liver　disease　is　a　common　disease　caused　by　alcoholism,　obesity,　and　diabetes,　resulting　in　triglyceride　accumulation　in　hepatocytes.　Kurtosis　coefficient,　a　measure　of　the　peakedness　of　the　probability　distribution,　has　been　applied　to　the　analysis　of　backscattered　statistics　for　characterizing　fatty　liver.　This　study　proposed　ultrasound　kurtosis　imaging　as　a　computer-aided　diagnosis　(CAD)　method　to　visually　and　quantitatively　stage　the　fatty　liver.　A　total　of　107　patients　were　recruited　to　participate　in　the　experiments.　The　livers　were　scanned　using　a　clinical　ultrasound　scanner　with　a　3.5-MHz　curved　transducer　to　acquire　the　raw　ultrasound　backscattered　signals　for　kurtosis　imaging.　The　kurtosis　image　was　constructed　using　the　sliding　window　technique.　Experimental　results　showed　that　kurtosis　imaging　has　the　ability　to　visualize　and　quantify　the　variation　of　backscattered　statistics　caused　by　fatty　infiltration.　The　kurtosis　coefficient　corresponding　to　liver　parenchyma　decreased　from　5.41　+/-　0.89　to　3.68　+/-　0.12　with　increasing　the　score　of　fatty　liver　from　0　(normal)　to　3　(severe),　indicating　that　fatty　liver　reduces　the　degree　of　peakedness　of　backscattered　statistics.　The　best　performance　of　kurtosis　imaging　was　found　when　discriminating　between　normal　and　fatty　livers　with　scores　＞=　1:　the　area　under　the　curve　(AUC)　is　0.92　at　a　cutoff　value　of　4.36　(diagnostic　accuracy　=86.9　%,　sensitivity　=86.7　%,　specificity　=87.0　%).　The　current　findings　suggest　that　kurtosis　imaging　may　be　useful　in　designing　CAD　tools　to　assist　in　physicians　in　early　detection　of　fatty　liver.