A Segmentation method for extraction of main arteries from coronary Cine-angiograms

Abstract

Coronary Cine- Angiogram (CCA) based subjective assessment of vascular malfunction is a preliminary diagnostic method in Cardiac clinical procedures. Even though there are many other medical image modalities available, improving the CCA method to objectively detect and assess the stenosis is a cost effective approach in Cardiac clinical procedures. Segmentation of Coronary Arteries (CA) is a basic and challenging area in such an endeavour. Hence, in this study we proposed a segmentation method to extract the major areas of CA based on Frangi's vessel enhancement filter and region growing segmentation method called flood fill. Experimental results of our proposed segmentation method have clearly proven its ability to extract the main CA almost completely. Moreover this proposed segmentation method possesses 93.73% average segmentation accuracy. Further, it detects the vessel path lines of the segmented frames using a thinning algorithm. The results obtained from this proposed segmentation method can be further enhanced to determine the functional severity of the CA and this study lays a foundation to improve the Coronary Angiogram image modality to do objective diagnosis of stenosis in future.