Precise estimation of coronary arteries from computed tomography angiography (CTA) data is one of the challenging problems. This study focuses on automatic delineation of coronary arteries from 3D CTA data that may assess the clinicians in identifying the coronary pathologies. In this work, we present a technique that effectively segments the complete coronary arterial tree under the guidance of initial vesselness response without relying on heavily manual operations. The proposed method isolates the coronary arteries with accuracy by using localized statistical energy model in two directions provided with an automated seed which ensures an optimal segmentation of the coronaries. The detection of seed is carried out by analyzing the shape information of the coronary arteries in three successive cross-sections. To demonstrate the efficiency of the proposed algorithm, the obtained results are compared with the reference data provided by Rotterdam framework for lumen segmentation and the level-set active contour based method proposed by Lankton et al. Results reveal that the proposed method performs better in terms of leakages and accuracy in completeness of the coronary arterial tree.