Contour and Centreline Tracking of Vessels from Angiograms using the Classical Image Processing Techniques

TL;DR

This paper presents a classical image processing pipeline for vessel edge and centerline detection in angiograms, combining vessel enhancement, thresholding, edge detection, and skeletonization, suitable for further vascular analysis.

Contribution

It introduces a step-by-step classical image processing method for vessel detection in angiograms, emphasizing simplicity and effectiveness for high-resolution images.

Findings

Accurate vessel detection on real angiogram data

Effective for images with good spatial resolution

Suitable for subsequent vessel analysis tasks

Abstract

This article deals with the problem of vessel edge and centerline detection using classical image processing techniques due to their simpleness and easiness to be implemented. The method is divided into four steps: the vessel enhancement which implies a non-linear filtering proposed by Frangi, the thresholding using Otsu method and the contour detection using the Canny edge detector due to its good performances for the small vessels and the morphological skeletonisation. The algorithms are tested on real data collected from a cardiac catheterism laboratory and it is accurate for images with good spatial resolution (512*512). The output image can be used for further processing in order to find the vessel length or its radius.