A FAST AND ACCURATE METHOD FOR AUTOMATIC CORONARY ARTERIAL TREE EXTRACTION IN ANGIOGRAMS
Rohollah Moosavi Tayebi, Puteri Suhaiza Binti Sulaiman, Rahmita Wirza, Mohd Zamrin Dimon, Suhaini Kadiman, Fatimah Khalid and Samaneh Mazaheri
DOI : 10.3844/jcssp.2014.2060.2076
Journal of Computer Science
Volume 10, Issue 10
Coronary arterial tree extraction in angiograms is an essential component of each cardiac image processing system. Once physicians decide to check up coronary arteries from x-ray angiograms, extraction must be done precisely, fast, automatically and including whole arterial tree to help diagnosis or treatment during the cardiac surgical operation. This application is very helpful for the surgeon on deciding the target vessels prior to coronary artery bypass graft surgery. Some techniques and algorithms are proposed for extracting coronary arteries in angiograms. However, most of them suffer from some disadvantages such as time complexity, low accuracy, extracting only parts of main arteries instead of the full coronary arterial tree, need manual segmentation, appearance of artifacts and so forth. This study presents a new method for extracting whole coronary arterial tree in angiography images using Starlet wavelet transform. To this end, firstly we remove noise from raw angiograms and then sharpen the coronary arteries. Then coronary arterial tree is extracted by applying a modified Starlet wavelet transform and afterwards the residual noises and artifacts are cleaned. For evaluation, we measure proposed method performance on our created data set from 4932 Left Coronary Artery (LCA) and Right Coronary Artery (RCA) angiograms and compared with some state-of-the-art approaches. The proposed method shows much higher accuracy 96% for LCA and 97% for RCA, higher sensitivity 86% for LCA and 89% for RCA, higher specificity 98% for LCA and 99% for RCA and also higher precision 87% for LCA and 93% for RCA angiograms.
© 2014 Rohollah Moosavi Tayebi, Puteri Suhaiza Binti Sulaiman, Rahmita Wirza, Mohd Zamrin Dimon, Suhaini Kadiman, Fatimah Khalid and Samaneh Mazaheri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.