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Nowadays, medical diagnostics using imageshaveconsiderable importance in many areas of medicine.Specifically, diagnoses of cardiac arteries can beperformed by means of digital images. Usually, thisdiagnostic is aided by computational tools. Generally, automated tools designed to aid in coronary heartdiseases diagnosis require the coronary artery treesegmentation. This work presents a method fora semi-automatic segmentation of the coronary artery tree in 2D angiograms. In other to achieve that, a hybridalgorithm based on region growing anddifferentialgeometry is proposed. For the validation of ourproposal, someobjective and quantitativemetricsaredefinedallowing us tocompareour methodwithanotheroneproposed in the literature.From the experiments, we observethat, in average, the proposedmethod here identifies about 90% of the coronary artery tree while themethod proposed by Schrijver &Slump(2002)identifies about 80%

international journal of computer science and information technology/international journal of computer science and information technology  (chennai. print)

A Novel Hybrid Method for the Segmentation of the Coronary Artery Tree in 2d Angiograms