The　homodyned-K　distribution　is　an　important　ultrasound　backscatter　envelope　statistics　model　of　physical　meaning,　and　the　parametric　imaging　of　the　model　parameters　has　been　explored　for　quantitative　ultrasound　tissue　characterization.　In　this　paper,　we　proposed　a　new　method　for　liver　fibrosis　characterization　by　using　radiomics　of　ultrasound　backscatter　homodyned-K　imaging　based　on　an　improved　artificial　neural　network　(iANN)　estimator.　The　iANN　estimator　was　used　to　estimate　the　ultrasound　homodyned-K　distribution　parameters　k　and　alpha　from　the　backscattered　radiofrequency　(RF)　signals　of　clinical　liver　fibrosis　(n　=　237),　collected　with　a　3-MHz　convex　array　transducer.　The　RF　data　were　divided　into　two　groups:　Group　I　corresponded　to　liver　fibrosis　with　no　hepatic　steatosis　(n　=　94),　and　Group　II　corresponded　to　liver　fibrosis　with　mild　to　severe　hepatic　steatosis　(n　=　143).　The　estimated　homodyned-K　parameter　values　were　then　used　to　construct　k　and　alpha　parametric　images　using　the　sliding　window　technique.　Radiomics　features　of　k　and　alpha　parametric　images　were　extracted,　and　feature　selection　was　conducted.　Logistic　regression　classification　models　based　on　the　selected　radiomics　features　were　built　for　staging　liver　fibrosis.　Experimental　results　showed　that　the　proposed　method　is　overall　superior　to　the　radiomics　method　of　uncompressed　envelope　images　when　assessing　liver　fibrosis.　Regardless　of　hepatic　steatosis,　the　proposed　method　achieved　the　best　performance　in　staging　liver　fibrosis　＞=　F1,　＞=　F4,　and　the　area　under　the　receiver　operating　characteristic　curve　was　0.88,　0.85　(Group　I),　and　0.85,　0.86　(Group　II),　respectively.　Radiomics　has　improved　the　ability　of　ultrasound　backscatter　statistical　parametric　imaging　to　assess　liver　fibrosis,　and　is　expected　to　become　a　new　quantitative　ultrasound　method　for　liver　fibrosis　characterization.