{"id":"W2025133043","doi":"10.5539/cis.v4n6p83","title":"Denoising, Segmentation and Characterization of Brain Tumor from Digital MR Images","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Computer vision; MATLAB; Filter (signal processing); Noise (video); Noise reduction; Pattern recognition (psychology); Image segmentation; Scale-space segmentation; Identification (biology); Image processing; Digital image processing; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003766454,0.0002749263,0.0003956591,0.001221981,0.000183919,0.0005286083,0.0003830927,0.0006390979,0.0005915679],"category_scores_gemma":[0.00117451,0.0002393772,0.0003797522,0.0006381162,0.0003964151,0.0005192435,0.0003248462,0.0003205586,0.0004770287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152035,"about_ca_system_score_gemma":0.0003588632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000677869,"about_ca_topic_score_gemma":0.001194602,"domain_scores_codex":[0.9997733,0.00003009353,0.00001536633,0.00003935075,0.0001274751,0.0000143762],"domain_scores_gemma":[0.999718,0.0001000806,0.00005065579,0.00004618874,0.00007259028,0.00001243345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000138968,0.0000487499,0.001494079,0.0004199606,0.00004042434,0.0002945521,0.0002111068,0.01629884,0.6575689,0.002726128,0.0009033294,0.3198549],"study_design_scores_gemma":[0.00002205625,0.0002279198,0.01326301,0.00006384292,0.0001239237,0.003443196,0.0002019008,0.3067111,0.6513504,0.005842685,0.01868761,0.0000623395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06998699,0.001101227,0.9263819,0.0001174172,0.00004170985,0.00005212672,0.00007515272,0.0006227508,0.001620696],"genre_scores_gemma":[0.2457491,0.001825262,0.7490079,0.00006806993,0.00006174363,0.00007107631,0.0002938576,0.0001628738,0.002760061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001221981,"threshold_uncertainty_score":0.001991868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281341086701335,"score_gpt":0.2400617673568325,"score_spread":0.2272483564898191,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}