X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing

TL;DR

This paper introduces a machine learning-based pixel classification and region-growing method for vessel segmentation in x-ray angiograms, achieving high accuracy by combining textural features and Random Forests.

Contribution

It presents a novel pixel-classification approach integrated with region-growing, utilizing multiple textural features and Random Forests for improved vessel segmentation accuracy.

Findings

Achieved 95.48% accuracy, the best in literature.

Outperformed existing unsupervised methods.

Effectively combined textural features with machine learning.

Abstract

This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each pixel. The approach also uses the ELEMENT methodology, which consists of creating a pixel-classification controlled by region-growing where the result of the classification affects further classifications of pixels. The Random Forests classifier is used to predict whether the pixel belongs to the vessel structure. The approach achieved the best accuracy in the literature (95.48%) outperforming unsupervised state-of-the-art approaches.