An Efficient Coronary Disease Diagnosis System Using Dual-Phase Multi-Objective Optimization and Embedded Feature Selection
نویسندگان
چکیده
Developmentsinhealthcaretechnologieshavesignificantlyenhancedspatialresolutionandimproved contrastresolution,permittinganalysisofadditionalsubtlestructuresthanformerlyattainable.An approachforAutomaticrecognitionandquantificationofcalcificationsfromarteriesincomputed tomography(CT)scansisdevelopedwhichisakeynecessityinplanningthetreatmentofindividuals withsuspectedcoronaryarterydisease.First,aDual-PhaseMulti-_objectiveOptimizationapproach usinganActiveContourModel-basedregion-growingtechniqueisdeveloped.Second,anembedded feature selectionmethod is developedwith an expert classifier to detect calcifiedobjects in the segmentedarterywithgreataccuracy.Finally,theAgatstonscoringmethodisutilizedtoquantify thelevelofcoronaryarterycalciumplaque.CoronaryCTimagesfromtheAS+CTscannerwitha slicethicknessof3mmwereobtainedfromclinicalpractice.Experimentalresultsdemonstratethat ourproposedmethodimprovestheaccuracyoflesiondetectionforbettertreatmentplanning. KEywORDS Calcium Object Detection, Coronary Artery Segmentation, Coronary Disease Diagnosis, Embedded Feature Selection, Multi-Objective Optimization
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ورودعنوان ژورنال:
- IJIIT
دوره 13 شماره
صفحات -
تاریخ انتشار 2017