The　image　reconstruction　in　diffuse　optical　tomography　(DOT)　is　a　typical　inverse　problem;　therefore,　regularization　techniques　are　essential　to　obtain　a　reliable　solution.　The　most　general　form　of　regularization　is　Tikhonov　regularization.　With　any　Tikhonov　regularized　reconstruction　algorithm,　one　of　the　crucial　issues　is　the　selection　of　the　regularization　parameter　that　controls　the　trade-off　between　the　regularized　solution　and　fidelity　to　the　given　sets　of　data.　Automatic　methods　such　as　L-curve,　generalized　cross-validation,　minimal　residual　method,　projection　error　method,　and　model　function　method　have　been　introduced　to　select　the　regularization　parameter　over　the　years.　However,　little　investigation　of　comparison　of　all　the　algorithms　has　been　reported　in　DOT.　The　performance　of　the　five　methods　for　choosing　regularization　parameter　is　comprehensively　compared,　and　advantages　and　limitations　are　discussed.　(C)　2016　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)