The　selection　of　stopping　time　(i.e.,　scale)　significantly　affects　the　performance　of　anisotropic　diffusion　filter　for　image　denoising.　This　paper　designs　a　Markov　random　field　(MRF)　scale　selection　model,　which　selects　scales　for　image　segments,　then　the　denoised　image　is　the　composition　of　segments　at　their　optimal　scales　in　the　scale　space.　Firstly,　statistics-based　scale　selection　criteria　are　proposed　for　image　segments.　Then　we　design　a　scale　selection　energy　function　in　the　MRF　framework　by　considering　the　scale　coherence　between　neighboring　segments.　A　segment-based　noise　estimation　algorithm　is　also　developed　to　estimate　the　noise　statistics　efficiently.　Experiments　show　that　the　performance　of　MRF　scale　selection　model　is　much　better　than　the　previous　global　scale　selection　schemes.　Combined　with　this　scale　selection　model,　the　anisotropic　diffusion　filter　is　comparable　to　or　even　outperform　the　state-of-the-art　denoising　methods　in　performance.　(C)　2010　Elsevier　Ltd.　All　rights　reserved.