Gold　immunochromatographic　strip　assay　provides　a　rapid,　simple,　single-copy　and　on-site　way　to　detect　the　presence　or　absence　of　the　target　analyte.　Comparing　to　the　traditional　qualitative　or　semi-quantitative　method,　a　completely　quantitative　interpretation　of　the　strips　can　lead　to　more　functional　information　than　the　traditional　qualitative　or　semi-quantitative　strip　assay.　This　paper　aims　to　develop　a　method　for　accurately　segmenting　the　test　line　and　control　line　of　the　gold　immunochromatographic　strip　(GICS)　image　for　quantitatively　determining　the　trace　concentrations　in　the　specimen.　The　canny　operator　as　well　as　the　mathematical　morphology　method　is　used　to　detect　and　extract　the　GICS　reading-window.　Then,　the　test　line　and　control　line　of　the　GICS　reading-window　are　segmented　by　the　cellular　neural　network　(CNN)　algorithm.　It　is　shown　that　the　CNN　offers　a　robust　method　for　accurately　segmenting　the　test　and　control　lines　via　adaptively　setting　the　threshold　value,　and　therefore　serves　as　a　novel　image　methodology　for　the　interpretation　of　GICS.　©　2012　Springer-Verlag.