In　this　paper,　a　novel　statistical　pattern　recognition　method　is　proposed　for　accurately　segmenting　test　and　control　lines　from　the　gold　immunochromatographic　strip　(GICS)　images　for　the　benefits　of　quantitative　analysis.　A　new　dynamic　state-space　model　is　established,　based　on　which　the　segmentation　task　of　test　and　control　lines　is　transformed　into　a　state　estimation　problem.　Especially,　the　transition　equation　is　utilized　to　describe　the　relationship　between　contour　points　on　the　upper　and　the　lower　boundaries　of　test　and　control　lines,　and　a　new　observation　equation　is　developed　by　combining　the　contrast　of　between-class　variance　and　the　uniformity　measure.　Then,　an　innovative　particle　filter　(PF)　with　a　hybrid　proposal　distribution,　namely,　deep-belief-network-based　particle　filter　(DBN-PF)　is　put　forward,　where　the　deep　belief　network　(DBN)　provides　an　initial　recognition　result　in　the　hybrid　proposal　distribution,　and　the　particle　swarm　optimization　algorithm　moves　particles　to　regions　of　high　likelihood.　The　performance　of　proposed　DBN-PF　method　is　comprehensively　evaluated　on　not　only　an　artificial　dataset　but　also　the　GICS　images　in　terms　of　several　indices　as　compared　to　the　PF　and　DBN　methods.　It　is　demonstrated　via　experiment　results　that　the　proposed　approach　is　effective　in　quantitative　analysis　of　GICS.