{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002772109,0.0000737483,0.00008559537,0.0002127411,0.00009994667,0.0004570194,0.0003206607,0.00001631434,0.00001084172],"category_scores_gemma":[0.00004073009,0.0000656778,0.00001046539,0.0003423489,0.0002938716,0.01840628,0.0002576644,0.00003862997,0.00000655445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001313241,"about_ca_system_score_gemma":0.00004326145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001216882,"about_ca_topic_score_gemma":7.417221e-8,"domain_scores_codex":[0.9991264,0.00001447398,0.0002940078,0.0001538835,0.0003042689,0.0001069077],"domain_scores_gemma":[0.9993598,0.00003493403,0.0001950608,0.0001588236,0.0001611645,0.00009015002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004903241,0.00002575183,0.002934969,0.00002708544,0.000003916283,8.549163e-7,0.01029948,4.491939e-7,0.09851906,0.003292355,0.0001631516,0.884728],"study_design_scores_gemma":[0.0004586904,0.0001914048,0.2481354,0.00006141337,0.000003558139,0.00002200479,0.0001316782,0.05099265,0.69836,0.001176787,0.0002363582,0.000230003],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2036902,0.000004592969,0.7956488,0.00006723596,0.00008668394,0.0001024088,0.000008542445,0.0000637984,0.000327797],"genre_scores_gemma":[0.747128,0.00001988316,0.2514412,0.001352512,0.00001896739,0.000005547608,0.00002551847,0.000002040472,0.000006372613],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.884498,"threshold_uncertainty_score":0.9953228,"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."}}