Smart Detection of Blockage in Coronary Artery in Angiography
More details
Hide details
Publication date: 2019-11-25
Eurasian J Anal Chem 2018;13(3):em2018183
In computer aided diagnosis of artery motion analysis coronary angiogram seg-mentation is of crucial importance. With vascular structures along with considerable varia-tion in intensities and noise it is challenging to develop an automated and accurate vessel segmentation algorithm. The proposed approach is an unsupervised approach with coro-nary angiography as the source and is used to extract the vascular centerlines and segment the vessels and detect the blockages in the coronary artery. Initially a preprocessing step is applied to enhance and remove the low frequency noise in the image based on a contrast limited adaptive histogram equalization and morphological filters. The vascular structure is extracted by using Morphological hessian based approach and region based Otsu thre-sholding. Two different scales are used to extract the wide and thin vessels. Then the ves-sel centerline is extracted. A branch detection algorithm is employed to find the bifurcation. The blockages are detected by considering the diameter along the cross sectional area of the vessel. The proposed system has been analyzed and the experimental results con-ducted on several images prove the efficiency of the proposed method producing an accu-racy of 96.98%.