The　scale　of　structure　element　is　especially　important　to　obtain　good　filtering　results　in　multiscale　morphological　filtering　(MMF)　method.　In　general,　the　optimal　scale　of　structure　element　is　set　to　be　a　fixed　value　in　traditional　morphological　filter,　therefore　it　is　difficult　to　extract　the　fault　feature　from　rolling　bearing　vibration　signal　effectively.　A　novel　multiscale　morphological　filtering　algorithm　is　proposed　based　on　information-entropy　threshold　(IET-MMF)　for　early　fault　detection　of　rolling　bearing.　Compared　with　traditional　MMF　method,　several　optimal　scales　of　structure　elements　are　achieved　according　to　the　energy　distribution　characteristic　of　different　vibration　signals.　The　information　entropy　theory　is　applied　to　quantify　the　analyzed　signals,　and　the　optimal　threshold　of　information　entropy　is　obtained　by　iterative　algorithm　to　ensure　integrity　of　useful　information.　The　simulation　and　rolling　bearing　experimental　analysis　results　show　that　the　IET-MMF　method　can　extract　fault　features　of　vibration　signals　effectively.