Colonoscopy　is　vital　for　detecting　colorectal　polyps,　which　are　closely　linked　to　colorectal　cancer.　Accurate　segmentation　of　polyps　in　colonoscopic　images　is　essential　for　diagnosis　and　surgical　planning　but　is　challenging　due　to　variability　in　polyp　size,　shape,　and　unclear　boundaries.　The　Segment　Anything　Model　(SAM)　has　shown　promise　in　polyp　segmentation　but　relies　heavily　on　user-provided　prompts　and　involves　a　large　number　of　parameters,　limiting　its　practicality　in　clinical　settings.　To　address　the　limitations　of　SAM　in　clinical　practice,　we　introduced　Low　Rank　and　Perturbation　Segment　Anything　Model　(LP-SAM)　to　improve　segmentation　accuracy　and　generalization　ability　while　reducing　the　parameter　count　and　complexity　of　user　input.　LP-SAM　showed　enhanced　generalization　and　a　lightweight　design,　making　it　more　suitable　for　clinical　applications　where　precise　user　input　may　not　always　be　feasible.　Comparative　evaluations　demonstrate　that　LP-SAM　outperforms　state-of-the-art　methods　on　datasets　such　as　CVC-ColonDB,　CVC-300,　and　ETIS.　©　2025　IEEE.