To　overcome　the　weak　edges　and　large　noise　of　flotation　froth　image,　and　to　solve　the　weakness　of　traditional　valley　detection　algorithm　on　different　kinds　of　bubble　segmentation　sizes,　a　froth　image　segmentation　method　was　proposed　based　on　Contourlet　transform　multi-scale　edge　enhancement　and　adaptive　valley　detection.　Firstly,　the　froth　image　was　decomposed　by　using　the　Contourlet　transfom　to　obtain　multi-scale　and　multi-direction　sub-band　coefficients.　Then,　thresholds　of　the　nonlinear　enhancement　function　were　determined　according　to　the　coefficients　of　each　scale　to　enhance　edges　and　suppress　the　noise.　Furthermore,　the　optimal　position　adjustment　strategy　and　parameter　setting　of　HS　were　improved　to　find　the　optimal　parameters　of　valley　detection　algorithm　and　to　detect　the　different　kinds　edges　of　bubble　image　size.　Finally,　segmentation　experiment　was　performed　and　obtained　result　was　further　improved　by　morphological　processing.　Experiments　show　that　the　proposed　method　effectively　detects　the　edges　of　different　type　of　bubbles　adaptively,　and　the　average　detection　efficiency　(DER)　is　91.2%　and　the　average　accuracy　(ACR)　is　90.6%,　which　is　much　better　than　that　of　traditional　methods.　This　method　has　high　precision,　good　adaptive　ability,　and　does　not　need　to　adjust　parameters　manually.　©　2016,　Science　Press.　All　right　reserved.