Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866912912669409280 |
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| author | Lahchim, Amal Athanasiou, Lambros |
| author_facet | Lahchim, Amal Athanasiou, Lambros |
| contents | 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 OCT image analysis and has potential applications in clinical decision support and real-time medical image processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15579 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning Lahchim, Amal Athanasiou, Lambros Computer Vision and Pattern Recognition Artificial Intelligence I.4.6; I.5.1; I.5.4 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 OCT image analysis and has potential applications in clinical decision support and real-time medical image processing. |
| title | Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.4.6; I.5.1; I.5.4 |
| url | https://arxiv.org/abs/2602.15579 |