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

E O Rodrigues¹, L O Rodrigues², J J Lima¹

¹Department of Academic Informatics (DAINF), Universidade Tecnologica Federal do Parana (UTFPR), Pato Branco, Parana, Brazil.

Biomedical Physics & Engineering Express

|July 13, 2021

Summary

This study introduces a novel pixel-classification method for segmenting vessels in X-ray angiograms, achieving 95.48% accuracy. The approach utilizes textural features and a region-growing technique, outperforming existing unsupervised methods.

Keywords:

angiographic cardiac learnings segmentation vessel x-ray

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Area of Science:

Medical Imaging
Computer Vision
Image Analysis

Background:

Accurate vessel segmentation in X-ray angiograms is crucial for diagnosing various cardiovascular conditions.
Existing unsupervised methods often struggle to achieve high accuracy and robustness.

Purpose of the Study:

To develop and evaluate a novel pixel-classification approach for precise vessel segmentation in X-ray angiograms.
To improve the accuracy and performance of automated vessel segmentation compared to state-of-the-art unsupervised methods.

Main Methods:

Extraction of textural features (anisotropic diffusion, Hessian matrix, mathematical morphology, statistics) from pixel neighborhoods.
Implementation of the ELEMENT methodology, a region-growing-controlled pixel classification that iteratively refines results.
Utilization of the Random Forests classifier for pixel-level vessel structure prediction.

Main Results:

The proposed pixel-classification approach achieved a state-of-the-art accuracy of 95.48% for vessel segmentation.
The method demonstrated superior performance compared to existing unsupervised segmentation techniques.
The ELEMENT methodology effectively enhanced classification accuracy through iterative refinement.

Conclusions:

The developed pixel-classification approach offers a highly accurate and effective solution for vessel segmentation in X-ray angiograms.
This method holds significant potential for improving the diagnosis and monitoring of vascular diseases.
The combination of advanced textural features and the ELEMENT methodology represents a promising direction for medical image analysis.