In　this　paper,　we　present　the　kernel　density　estimation　(KDE)-based　parallelized　ultrasound　entropy　imaging　and　apply　it　for　hepatic　steatosis　characterization.　A　KDE　technique　was　used　to　estimate　the　probability　density　function　(PDF)　of　ultrasound　backscattered　signals.　The　estimated　PDF　was　utilized　to　estimate　the　Shannon　entropy　to　construct　parametric　images.　In　addition,　the　parallel　computation　technique　was　incorporated.　Clinical　experiments　of　hepatic　steatosis　were　conducted　to　validate　the　feasibility　of　the　proposed　method.　Seventy-two　participants　and　204　patients　with　different　grades　of　hepatic　steatosis　were　included.　The　experimental　results　show　that　the　KDE-based　entropy　parameter　correlates　with　log(10)　(hepatic　fat　fractions)　measured　by　magnetic　resonance　spectroscopy　in　the　72　participants　(Pearson＇s　r　=　0.52,　p　＜　0.0001),　and　its　areas　under　the　receiver　operating　characteristic　curves　for　diagnosing　hepatic　steatosis　grades　＞=　mild,　＞=　moderate,　and　＞=　severe　are　0.65,　0.73,　and　0.80,　respectively,　for　the　204　patients.　The　proposed　method　overcomes　the　drawbacks　of　conventional　histogram-based　ultrasound　entropy　imaging,　including　limited　dynamic　ranges　and　histogram　settings　dependence,　although　the　diagnostic　performance　is　slightly　worse　than　conventional　histogram-based　entropy　imaging.　The　proposed　KDE-based　parallelized　ultrasound　entropy　imaging　technique　may　be　used　as　a　new　ultrasound　entropy　imaging　method　for　hepatic　steatosis　characterization.