Performance Comparison of Vesselness Measures for Segmentation of Coronary Arteries in 2D Angiograms

Abstract

Objectives: In this paper, we aim to compare four different vesselness filters and propose a framework for segmenting coronary arteries from 2D angiograms with the aim of extracting accurate centerlines. Methods/Statistical analysis: Performance measures including noise suppression, edge smoothness, branch disconnection and centerline smoothness are used for comparing the performance of vesselness functions. Moreover, we have performed the segmentation of coronary arteries from the obtained vesselness measure using globalized region based active contour followed by median filtering to remove the artifacts such as unsmoothed edges. Findings: The study reveals that Frangi’s vesselness performs well in suppressing the background noise, whereas, the other vesselness measures perform better at enhancing vessels throughout crossings and bifurcations. Except Frangi’s vesselness, edges obtained by all the compared vesselness measure are prone to uneven and rough edges that will eventually lead to the extraction of wrong centerlines. Application/Improvements: Based on the findings, we have presented a segmentation method that produces more enhanced and smooth edges of coronary arteries and leads to the extraction of the smooth centerlines.