{"id":"W2096486527","doi":"10.1109/iembs.2009.5332777","title":"De-noising of SPECT images via optimal thresholding by wavelets","year":2009,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Thresholding; Noise reduction; Wavelet; Imaging phantom; Artificial intelligence; Single-photon emission computed tomography; Noise (video); Computer science; Signal-to-noise ratio (imaging); Image quality; Spect imaging; Reduction (mathematics); Pattern recognition (psychology); Computer vision; Image (mathematics); Mathematics; Nuclear medicine; Medicine; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000916759,0.0003277999,0.0004861467,0.000378757,0.0001311019,0.0004819239,0.0003189137,0.0004975176,0.0005464176],"category_scores_gemma":[0.001973005,0.000238258,0.0005414899,0.0004183695,0.0003607645,0.0004283228,0.0003858349,0.0004956253,0.0002302093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000196375,"about_ca_system_score_gemma":0.0002680598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003789855,"about_ca_topic_score_gemma":0.0004766228,"domain_scores_codex":[0.9997545,0.00006584156,0.00002003932,0.00003949135,0.00009306221,0.00002702055],"domain_scores_gemma":[0.9996465,0.0001624328,0.00004335469,0.00005457264,0.0000809773,0.00001199621],"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":[0.000535554,0.00009440257,0.001352461,0.0002400902,0.00006482528,0.000196958,0.000216667,0.1495705,0.6111526,0.007553707,0.0005257199,0.2284965],"study_design_scores_gemma":[0.00001930083,0.0001307149,0.001333848,0.00001838636,0.00003228538,0.0001891122,0.00002933213,0.8409518,0.154057,0.001931049,0.001289751,0.00001737457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1070823,0.0001821312,0.891781,0.00005292522,0.00002143268,0.00001970946,0.00002801973,0.0001684291,0.0006640314],"genre_scores_gemma":[0.3588465,0.0004072494,0.6395878,0.00002260745,0.00001537982,0.00004165573,0.00008999552,0.00009570951,0.0008931],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.000916759,"threshold_uncertainty_score":0.004848361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302908355776516,"score_gpt":0.2760374417899014,"score_spread":0.2630083582321363,"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."}}