Gold　immunochromatographic　strip　assay　provides　a　rapid,　simple,　single-copy　and　on-site　way　to　detect　the　presence　or　absence　of　the　target　analyte.　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,　which　can　lead　to　more　functional　information　than　the　traditional　qualitative　or　semi-quantitative　strip　assay.　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,　where　the　template　parameters　of　the　CNN　are　designed　by　the　switching　particle　swarm　optimization　(SPSO)　algorithm　for　improving　the　performance　of　the　CNN.　It　is　shown　that　the　SPSO-based　CNN　offers　a　robust　method　for　accurately　segmenting　the　test　and　control　lines,　and　therefore　serves　as　a　novel　image　methodology　for　the　interpretation　of　GICS.　Furthermore,　quantitative　comparison　is　carried　out　among　four　algorithms　in　terms　of　the　peak　signal-to-noise　ratio.　It　is　concluded　that　the　proposed　CNN　algorithm　gives　higher　accuracy　and　the　CNN　is　capable　of　parallelism　and　analog　very-large-scale　integration　implementation　within　a　remarkably　efficient　time.