In　lung　cancer　computer-aided　diagnosis　(CAD),　the　correct　segmentation　of　the　lung　parenchyma　is　particularly　important.　In　order　to　reduce　the　detection　area,　save　computational　time　and　improve　accuracy,　lung　tissue　needs　to　be　extracted　in　advance.　An　improved　method　of　maximum　inter-class　variance　(OTSU)　combined　with　morphological　operations　is　proposed.　First,　the　original　CT　image　is　preprocessed　by　filtering,　denoising,　image　enhancement,　and　adaptive　threshold　binarization;　then　the　connecting　area　marker　obtains　the　outline,　using　OTSU-based　improvement　algorithm　to　remove　interference　such　as　trachea　lung　fluid,　separates　the　lung　essence　and　background,　uses　the　column　scanning,　regional　color　marking　and　effectively　separate　the　left　and　right　lung　leaf　adhesion　and　finally　uses　a　series　of　morphological　operations　to　repair　the　extracted　lung　essence.　830　CT　images　were　selected　from　the　public　database　LIDC,　and　were　successfully　segmented　using　this　proposed　method,　with　an　average　accuracy　of　97.56　percent,　an　average　recall　rate　that　reaches　99.29　percent,　and　a　Dice　similarity　coefficient　of　98.42　percent.　©　2020　ACM.