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[2602.15579] Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning
Summary
This paper presents a machine learning-based pipeline for automated segmentation and classification of vessels in Intracoronary Optical Coherence Tomography (OCT) images, achieving high accuracy and efficiency.
Why It Matters
The study addresses the challenges of noise and artifacts in OCT imaging, which are critical for accurate coronary vessel analysis. By automating the classification process, this research has significant implications for improving clinical decision-making and enhancing real-time medical imaging capabilities.
Key Takeaways
- Proposes a fully automated pipeline for OCT image analysis.
- Achieves high classification accuracy (99.68%) with minimal manual input.
- Integrates various machine learning techniques for effective vessel segmentation.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.15579 (cs) [Submitted on 17 Feb 2026] Title:Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning Authors:Amal Lahchim, Lambros Athanasiou View a PDF of the paper titled Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning, by Amal Lahchim and Lambros Athanasiou View PDF Abstract:Intracoronary Optical Coherence Tomography (OCT) enables high-resolution visualization of coronary vessel anatomy but presents challenges due to noise, imaging artifacts, and complex tissue structures. This paper proposes a fully automated pipeline for vessel segmentation and classification in OCT images using machine learning techniques. The proposed method integrates image preprocessing, guidewire artifact removal, polar-to-Cartesian transformation, unsupervised K-means clustering, and local feature extraction. These features are used to train Logistic Regression and Support Vector Machine classifiers for pixel-wise vessel classification. Experimental results demonstrate excellent performance, achieving precision, recall, and F1-score values up to 1.00 and overall classification accuracy of 99.68%. The proposed approach provides accurate vessel boundary detection while maintaining low computational complexity and requiring minimal manual annotation. This method offers a reliable and efficient solution for automated OC...
