Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning
This provides an efficient solution for clinical decision support in medical imaging, though it appears incremental as it combines existing machine learning techniques.
The paper tackled the problem of automated vessel segmentation and classification in intracoronary Optical Coherence Tomography (OCT) images, achieving high performance with precision, recall, and F1-scores up to 1.00 and an overall accuracy of 99.68%.
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.