We　propose　a　new　structure-adaptive　anisotropic　filtering　scheme　based　on　the　local　structure　tensor.　We　utilize　the　local　structure　tensor　to　measure　image　local　anisotropic　features　and　estimate　the　orientation　of　image　structures,　and　these　informations　are　then　used　to　shape　and　control　the　anisotropic　Gaussian　kernel.　The　proposed　filter　denoises　noisy　images　while　image　structures　such　as　corners,　junctions　and　edges　are　well　preserved.　Our　experimental　results　clearly　show　that　the　proposed　scheme　outperforms　some　other　adaptive　filters　such　as　the　adaptive　Wiener　filter,　Weickert＇s　edge　enhancing　diffusion　(EED)　filter　and　Yang＇s　structure　adaptive　anisotropic　filter　in　terms　of　both　mean　square　errors　(MSE)　and　visual　quality,　and　the　one　based　on　the　nonlinear　structure　tensor　(NLST)　can　give　much　better　denoising　results　than　that　based　on　the　linear　structure　tensor　(LST),　particularly　in　edge　regions.