{"id":"W1999316653","doi":"10.1109/tmm.2013.2244870","title":"Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":472,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Contourlet; Computer science; Image fusion; Artificial intelligence; Modalities; Contrast (vision); Medical imaging; Phase congruency; Fusion rules; Computer vision; Image (mathematics); Pattern recognition (psychology); Fuse (electrical); Modality (human–computer interaction); Wavelet transform; Engineering","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":[0.0005550967,0.0003942509,0.0004458153,0.0007584987,0.0001605172,0.000594733,0.0003651118,0.0005856826,0.001212769],"category_scores_gemma":[0.001422924,0.0001367901,0.0005259198,0.0006853525,0.0003641984,0.0008678805,0.0007147197,0.0004859042,0.00051792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002368829,"about_ca_system_score_gemma":0.0003022792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005067796,"about_ca_topic_score_gemma":0.0004696437,"domain_scores_codex":[0.9997372,0.00005689141,0.00001536158,0.00004066094,0.0001328687,0.0000170034],"domain_scores_gemma":[0.9997265,0.00008318524,0.00003912222,0.00004623787,0.00009113225,0.00001384988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006212316,0.0001098208,0.001059887,0.0003179574,0.0001124774,0.0007261891,0.0002493752,0.1007265,0.3540399,0.02046978,0.003800558,0.5177664],"study_design_scores_gemma":[0.00002190653,0.0001809954,0.001798096,0.0000257653,0.00006462477,0.00122016,0.00006548938,0.842657,0.1391193,0.00811593,0.006688718,0.00004204853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02256168,0.0002705317,0.9748127,0.0001464729,0.0000388138,0.00004503403,0.00007624164,0.0003181644,0.001730413],"genre_scores_gemma":[0.354598,0.000939793,0.6407675,0.0002172698,0.0001062349,0.0001112668,0.000499943,0.0001066044,0.002653476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001212769,"threshold_uncertainty_score":0.004057109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004383614316694057,"score_gpt":0.2282344904893442,"score_spread":0.2238508761726501,"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."}}